Check out our article and text analysis API to get interesting insights and improve your applications.

This is a pretrained model on One Billion Word Benchmark. In this release, we open source a model trained on the One Billion Word Benchmark (http://arxiv.org/abs/1312.3005), a large language corpus in English which was released in 2013. This dataset contains about one billion words, and has a vocabulary size of about 800K words. It contains mostly news data. Since sentences in the training set are shuffled, models can ignore the context and focus on sentence level language modeling.

DESCRIPTION Are you experienced at applying machine learning to big-data tasks? Are you excited by analyzing and modeling terabytes of text, images, and other types of data to solve real-world problems? We love data and we have lots of it. Were looking for top scientists capable of using machine learning and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. The central machine learning organization at Amazon has multiple positions available for applied scientists in Seattle, Palo Alto, Berlin, and Bangalore for machine learning experts at all career stages, from graduating doctoral students to internationally renowned researchers and practitioners.

I recently watched a movie called'Her' starring Joaquin Phoenix and Scarlett Johansson. Set in the future the beautiful, strange and emotional story of a letter writer, Theodore Twombly (Phoenix) who, after separating from his girlfriend, downloads an intelligent computer operating system (OS) personified through a female voice, 'Samantha' played by Johansson. Director Spike Jonze conceived the idea of the film after reading about Cleverbot, a web application that uses an artificial intelligence algorithm to have conversations with humans. Twombly had quite simply created his'perfect' woman. He had answered a few questions; What gender was he?

Technology has greatly affected our lives over the years. Now, a study from Stanford University has revealed how artificial intelligence (A.I.) may change people's lifestyle -- from transportation, employment, health and household chores -- by the year 2030. According to a Stanford report entitled "Artificial Intelligence and Life in 2030," A.I. will be more common and will, in fact, be helpful in terms of improving the economy and everyday quality of life. "A.I. technologies can be reliable and broadly beneficial. Being transparent about their design and deployment challenges will build trust and avert unjustified fear and suspicion," said Barbara Grosz, a computer scientist from Harvard and chair of AI100 (Stanford's One Hundred Year Study on Artificial Intelligence) via Computer World. The study reveals that in 2030, autonomous vehicles will roam the streets.

At our recent bots, ai & messaging meetup we talked through our thoughts on simplifying the bot strategy and design process -- from objectives to conversation trees and beyond. Here is a roundup of our latest thoughts on designing and building effective messaging applications, bots and more. The first step in the bot planning process is deciding what the bot will do for the bot's creator, and what it will do for the bot's users. There's no point in in making something no-one will want to use, and no point in making something that does nothing for your business. So define both sets of objectives clearly and look for how these could overlap within a messaging application.

Ladies and gentlemen, place your bets, today we are going to do our best to beat one of the most frightening opponents that you can face in finance: a monkey. As you probably already know, in this blog we are all quite obsessed with predicting trends and returns, you can find other nice attempts in'Markov Switching Regimes say… bear or bullish?' by mplanaslasa or'Predict returns using historical patterns' by fjrodriguez2. Today, we are trying to predict the sign of tomorrow's return for different currency pairs, and I can assure you that a monkey making random bets on the sign and getting it right 50% of the time is going to be a tough benchmark. We are going to use an off the shelf machine learning algorithm, the support vector classifier. Support Vector Machines are an incredibly powerful method to solve regression and classification tasks.

Much of the discussion around artificial intelligence has been around how many jobs will be lost as a result of automation rather than any discussion about the ways AI will improve our ability to work safely and effectively. A good example of this comes from research undertaken by the Jet Propulsion Laboratory that looked at how AI can be used to collect building data to help keep firefighters safe when they enter an inferno. They developed a system called AUDREY, which stands for Assistant for Understanding Data through Reasoning, Extraction and synthesis, in conjunction with the Department of Homeland Security. AUDREY is capable of tracking the team of firefighters as they enter a building, all the time sending them data that is personalized to their location in the facility. It is even capable of giving them recommendations on where and what to do next.

Microsoft has broadened open-source access to its Computational Network Toolkit, in a move that underscores the ongoing arms race around machine learning technology. CNTK had initially been made available on Microsoft's Codeplex site in April 2015 through under an academic license, but now it has been... Constellation Research, Constellation SuperNova Awards, Connected Enterprise, and the Constellation Research logo are trademarks of Constellation Research, Inc. All other products and services listed herein are trademarks of their respective companies.

Machine learning has been put to many uses that involve analyzing Big Data to solve issues or address opportunities. It's been applied to delivering personalized video or article recommendations, handling fraud detection, clustering news, and predicting customer churn, for example. Now it – along with other computer science breakthroughs – is playing a role in a solution aimed at reinventing the science of databases, the places where all the Big Data that informs analytics is stored from the start. Deep Engine from Deep Information Sciences "is an adaptive database kernel and information orchestration system that leverages machine learning to completely transform how scalability and performance are achieved," says chief strategy officer Chad Jones. The company initially is targeting the Deep Engine hybrid transactional and analytical processing (HTAP) solution that handles multiple simultaneous workloads on the fly for the structured data market.