We're excited to introduce the latest report and prototype from our machine intelligence R&D group! In this iteration, we explore summarization, or neural network techniques for making unstructured text data computable. Making language computable has been a goal of computer science research for decades. Historically, it has been a challenge to merely collect and store data. But it's now so cheap to store data that we often have the opposite problem: once we've data, how should we analyze it to find meaning and insights?

In this article we introduce modern statistical machine learning and bioinformatics approaches that have been used in learning statistical relationships from big data in medicine and behavioral science that typically include clinical, genomic (and proteomic) and environmental variables. Every year, data collected from biomedical and behavioral science is getting larger and more complicated. Thus, in medicine, we also need to be aware of this trend and understand the statistical tools that are available to analyze these datasets. Many statistical analyses that are aimed to analyze such big datasets have been introduced recently. However, given many different types of clinical, genomic, and environmental data, it is rather uncommon to see statistical methods that combine knowledge resulting from those different data types. To this extent, we will introduce big data in terms of clinical data, single nucleotide polymorphism and gene expression studies and their interactions with environment. In this article, we will introduce the concept of well-known regression analyses such as linear and logistic regressions that has been widely used in clinical data analyses and modern statistical models such as Bayesian networks that has been introduced to analyze more complicated data. Also we will discuss how to represent the interaction among clinical, genomic, and environmental data in using modern statistical models. We conclude this article with a promising modern statistical method called Bayesian networks that is suitable in analyzing big data sets that consists with different type of large data from clinical, genomic, and environmental data.

Welcome to Visual Evidence, a new regular series about visualization in the real world! We'll take a look at unexpected datasets, cool design solutions or insightful graphics. We'll find examples of how visual information can help us solve real-world problems or save us from our own mistakes. And we'll illustrate all these ideas with charts, sketches, and of course, plenty of gifs. Chances are, you probably think your mind works pretty well. It might lead you astray now and then, but usually it helps you make good decisions and remember things reliably. At the very least, you're probably confident that it doesn't change depending on the time of day or what you had to eat.

Until recently, machine learning was an esoteric discipline, used only by a few who understood the algorithms and had access to tons of data on which to employ it. But with big data technology becoming a commodity and algorithms easier to use, machine learning has moved out of the shadows and into the hands of citizen developers and regular users. Here are four key steps to that development during the course of 2015. "Easier" is a relative term, but anything that simplifies the amount of work needed to get useful results from the intrinsic complexity of machine learning is a boon. Spark, the in-memory big-data processing toolkit, simplified machine learning with its relatively straightforward programming model -- available to more than Java users alone -- and its built-in array of machine learning algorithms.

Language is a system or method of communication as defined by the Oxford Dictionary. Humans acquire skills based on cognition, patterns and connections, while machines learn through programming and algorithms. After years of technological advancement and progress in Artificial Intelligence (AI), language acquisition and understanding is still the number one thing that sets humans apart from machines. An insight into human language acquisition versus machine AI will provide a clear picture of language learning for humans and machines. Advancement of AI is based on understanding different attributes of humans and replicating it on machines.

For companies of all sizes around the globe, advances in logistics processes and technologies can have a dramatic effect on operational costs and, ultimately, the bottom line. Faster, more affordable delivery systems, more efficient warehousing options, and innovations in customs clearance processes are just a few ways that improvements in logistics can help businesses better connect with their customers and suppliers, all while saving time and money. Business leaders who understand what changes are coming down the logistics roadway are at a distinct advantage; they can plan for and adopt new technologies and systems as soon as possible, keeping up with – or charging ahead of – the competition. That's where The DHL Logistic Trend Radar comes in. Now in its fourth year, the Logistics Trend Radar is a powerful tool for understanding and anticipating new logistics advances.

In March, the Toyota Research Institute bought up Cambridge-based startup Jaybridge Robotics, and according to Tech Insider, they may be expanding with another famous Massachusetts company: Google's Boston Dynamics, the maker of the Big Dog and Atlas robots. Google has been talking about selling the Waltham-based Boston Dynamics, for a couple of months now, with companies such as Toyota Research Institute and Amazon.com Around that time, TRI, which seeks to create a car that is incapable of crashing, announced a deal to acquire Jaybridge, a 16-member software engineering company, in order to add more expertise to creating "autonomous vehicle products." Tech Insider noted that the deal hasn't been finalized, but that "the ink is nearly dry." On the other side of the alleged deal is Google, which acquired the Waltham-based company in 2013.

This paper proposes a relational learning based approach for discovering strategies in volleyball matches based on optical tracking data. In contrast to most existing methods, our approach permits discovering patterns that account for both spatial (that is, partial configurations of the players on the court) and temporal (that is, the order of events and positions) aspects of the game. We analyze both the men's and women's final match from the 2014 FIVB Volleyball World Championships, and are able to identify several interesting and relevant strategies from the matches.

Machine learning algorithms are a very large part of machine learning. You have to understand how they work to make any progress in the field. In this post you will discover a 14-part machine learning algorithms mini course that you can follow to finally understand machine learning algorithms. We are going to cover a lot of ground in this course and you are going to have a great time. Machine Learning Algorithms Mini-Course Photo by Jared Tarbell, some rights reserved. Before we get started, let's make sure you are in the right place. This mini-course will take you on a guided tour of machine learning algorithms from foundations and through 10 top techniques.

A photo shows the logo of Renren.com. Will robots take over our world? These questions, which once seemed irrelevant, now frequently come into our minds with the advancement of Artificial Intelligence (AI). A recent report shows that there are almost no active users left on Renren, as advertising accounts keep pushing uninteresting contents and the system keeps recommending other people's posts that were so "yesterday". Some have jokingly said this must be what will happen to our world after it is taken by AI.