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.

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.

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.

Especially when those ideas stand in opposition to technological progress. I'm used to hearing, and quickly dismissing, fears related to our ongoing developments, especially when they come without factual basis or understanding of the underlying societal and technological aspects implicated. So keep all of that in mind when I tell you that I'm terrified of the future. Because I'm about to be fired and replaced by a robot. Yes, I'm afraid I've fallen off the bandwagon once again, knocked my head, and started to agree with some of the pundits you're hearing in the press. While I sense most people have no inkling of the huge tidal wave of change that's coming towards us, some of its effects are already starting to be felt.

Recently I visited the Innorobo show in Paris, a gathering of robot companies from around the world, which brought together industrial robots, service robots, toy robots, family robots, and many other robot types, all under one roof. One of the key highlights was SoftBank Robotics launching Pepper Partners Europe, inviting developers and companies in Europe to build applications for SoftBank's flagship robot, Pepper. This initiative is part of SoftBank's expansion of Pepper outside Japan, where more than 3,000 Pepper robots have already been deployed at over 1,000 companies. SoftBank Robotics, largely composed of the French robotics pioneer Aldebaran (of which Softbank owns 95%), has more than 500 employees globally, with the majority based in Paris at the Aldebaran facility. SoftBank has big ambitions for Pepper, and with more than 20,000 Pepper robots deployed worldwide in both consumer and enterprise environments, SoftBank is easily the leading player in this space.

Apple now has the tech in place to give its digital assistant a big boost thanks to a UK-based company called VocalIQ it bought last year. In fact, it was so impressive that Apple bought VocalIQ before the company could finish and release its smartphone app. After the acquisition, Apple kept most of the VocalIQ team and let them work out of their Cambridge office and integrate the product into Siri. Before Apple bought the company, VocalIQ tested its product against Siri, Google Now, and Cortana, and the results were impressive. Users asked each AI questions using normal language, not the robotic commands you're used to using with digital assistants.

Google Home is an always-on speaker coming later this year. LOS ANGELES - We know that Big Brother has been watching our every move for years--but are you ready to have your home conversations monitored as well? Following on the footsteps of Amazon's smash Echo connected speaker, which reads the weather and news, plays music, and turns lights on and off, based on your voice instructions, Google is set to launch its rival product, Google Home, later this year. And this week reports surfaced saying Apple could be joining the fray with its own speaker featuring the Siri personal digital assistant from the iPhone. Which begs the question--how do you feel about buying a connected speaker for the kitchen that could listen to every word you're saying?