WIRE)--International Data Corporation (IDC) has published a 2016 IDC Innovators report recognizing pioneering players in the machine learning-based text analytics market. IDC Innovators are companies with under 50M in revenue that offer an inventive technology and/or groundbreaking new business model. Kira Systems, Loop AI Labs, NetBase, and Taste Analytics were all named as IDC Innovators in the machine learning-based text analytics market for 2016. "Organizations are continually looking to improve their handling of data, especially unstructured data, given the explosion of information that is available via the Internet today," said David Schubmehl, Research Director, IDC's Content Analytics, Discovery and Cognitive Systems research. "Understanding and utilizing this human-generated data is a significant challenge for most organizations and the use of machine learning based text analytics is rapidly becoming the best approach to dealing with this type of data."

SUMMARY: Artificial intelligence (AI) technologies offer great promise for creating new and innovative products, growing the economy, and advancing national priorities in areas such as education, mental and physical health, addressing climate change, and more. Like any transformative technology, however, AI carries risks and presents complex policy challenges along a number of different fronts. The Office of Science and Technology Policy (OSTP) is interested in developing a view of AI across all sectors for the purpose of recommending directions for research and determining challenges and opportunities in this field. The views of the American people, including stakeholders such as consumers, academic and industry researchers, private companies, and charitable foundations, are important to inform an understanding of current and future needs for AI in diverse fields. The purpose of this RFI is to solicit feedback on overarching questions in AI, including AI research and the tools, technologies, and training that are needed to answer these questions.

The notion of applying deep learning techniques to medical imaging data sets is a fascinating and fast-moving area. In fact, in a recent issue of IEEE's Transactions on Medical Imaging journal, there's a fantastic guest editorial on deep learning in medical imaging, that provides an overview of current approaches, where the field is headed, and what sort of opportunities exist. As such, we pulled out some of our favorite nuggets from this article and summarize/extend upon them in Q&A form, so they're more easily digestible. Most interpretations of medical images are performed by physicians; however, image interpretation by humans is limited due to its subjectivity, large variations across interpreters, and fatigue. One way is via transfer learning, which has been used to overcome the lack of large labeled data sets in medical imaging.

This issue features expanded versions of articles selected from the 2015 AAAI Conference on Innovative Applications of Artificial Intelligence held in Austin, Texas. We present a selection of four articles describing deployed applications plus two more articles that discuss work on emerging applications.

This article reviews the last two IEEE Conference on Computational Intelligence and Games (CIG) StarCraft Artificial Intelligence (AI) Competitions organized by the authors; these were the fourth and fifth in a series of annual competitions initiated in 2011. StarCraft AI Competitions have been hosted in conjunction with three different events: the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), CIG, and Student StarCraft AI Tournament (SSCAIT). The purpose of these competitions is to design bots that are able to autonomously and successfully play the StarCraft game by implementing real-time strategies. Recent results reveal the promising use of AI techniques in creating successful AI entries, but there is room for improvement with respect to the bots’ ability to adapt and learn to defeat humans and scripted AI bots.

I have been invited to write a book chapter on lexical choice for translators (contact me if you want to see a preprint). To get acquainted on this audience different from my usual computer science I read a few papers on professional translators use of technology. Two of them are quite interesting and I recommend them not only because they make for a good read and they have implications outside translation: Translation Skill-sets in a Machine-translation Age by Anthony Pym (2013) and Is Machine Translation Post-editing Worth the Effort?: A Survey of Research into Post-editing and Effort by Maarit Koponen (2016). This search finished by reading a short ebook by researchers at the MIT Center for Digital Business titled Race Against the Machine: How the Digital Revolution Is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy. In that book plus the papers there's this call for humans, if we want to remain employed, to hybridize our work and to seek out ways to work with the computer as some sort of partnership.

Editor's Note: Don't miss our upcoming Free Code Friday on July 1st. Carol will give an overview of machine learning with Apache Spark's MLlib, and you'll also learn how MLlib decision trees can be used to predict flight delays. Decision trees are widely used for the machine learning tasks of classification and regression. In this blog post, I'll help you get started using Apache Spark's MLlib machine learning decision trees for classification. In general, machine learning may be broken down into two classes of algorithms: supervised and unsupervised.

One of the key insights that legendary physicist and Nobel Prize laureate Richard Feynman had was that quantum mechanics (the branch of physics that deals with subatomic particles, uncertainty principle, and many other concepts beyond classic physics) is just way too complicated to simulate using traditional computers. Nature, of course, can handle these complex calculations -- computers however can't do those same calculations (or would take a prohibitively long time and amount of resources to do so). But this isn't just about being able to do more with computers in a faster (or smaller) way: It's about solving problems that we couldn't solve with traditional computers; it's about a difference of kind not just degree. So what is a quantum computer and "qubits" -- especially as compared to a traditional computer and bits? And besides speed of processing, what are some of the new applications that wouldn't have been possible before? From how traditional computers work and quantum computers will work to why this all matters, a16z Deal and Research team head Frank Chen walks us through the basics of quantum computing in this slide presentation.

Today, I'm going to explain in plain English the top 10 most influential data mining algorithms as voted on by 3 separate panels in this survey paper. Once you know what they are, how they work, what they do and where you can find them, my hope is you'll have this blog post as a springboard to learn even more about data mining. In order to do this, C4.5 is given a set of data representing things that are already classified. A classifier is a tool in data mining that takes a bunch of data representing things we want to classify and attempts to predict which class the new data belongs to. Sure, suppose a dataset contains a bunch of patients.

Everyone wants to keep ahead of the technology curve, whether it's being'in-the-know' or as a vital part of your business, to allow for effective future planning or perhaps to start building systems and products based on that technology. So how do you know what that next big thing will be? Were people betting on touch interfaces in 2002? Not me, I was in a Macromedia Flash phase and couldn't possibly imagine anything beating a vector shape moving across the screen at 12 frames per second. I am not that same guy anymore.