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.

Data analytics firm Orbital Insight is partnering with the World Bank to test technology that could help measure global poverty using satellite imagery and artificial intelligence. The new partnership will test the use of AI to supplement these surveys and increase the accuracy of poverty data. Orbital said its AI software will analyze satellite images to see if characteristics such as building height and rooftop material can effectively indicate wealth. The pilot study will be conducted in Sri Lanka. If successful, the World Bank hopes to scale it worldwide.

Editor's Note: While Andrew Ng, chief scientist at Baidu was delivering his ISC keynote this morning on how HPC is supercharging AI, his colleague Greg Diamos, research scientist at Baidu's Silicon Valley AI Lab, was preparing to present a paper on GPU-based deep learning at the 33rd International Conference on Machine Learning in New York. Greg Diamos, senior researcher, Silicon Valley AI Lab, Baidu, is on the front lines of the reinvigorated frontier of machine learning. Before joining Baidu, Diamos was in the employ of NVIDIA, first as a research scientist and then an architect (for the GPU streaming multiprocessor and the CUDA software). Given this background, it's natural that Diamos' research is focused on advancing breakthroughs in GPU-based deep learning. Ahead of the paper he is presenting, Diamos answered questions about his research and his vision for the future of machine learning.

We present a framework that couples the syntax and semantics of natural language sentences in a generative model, in order to develop a semantic parser that jointly infers the syntactic, morphological, and semantic representations of a given sentence under the guidance of background knowledge. To generate a sentence in our framework, a semantic statement is first sampled from a prior, such as from a set of beliefs in a knowledge base. Given this semantic statement, a grammar probabilistically generates the output sentence. A joint semantic-syntactic parser is derived that returns the $k$-best semantic and syntactic parses for a given sentence. The semantic prior is flexible, and can be used to incorporate background knowledge during parsing, in ways unlike previous semantic parsing approaches. For example, semantic statements corresponding to beliefs in a knowledge base can be given higher prior probability, type-correct statements can be given somewhat lower probability, and beliefs outside the knowledge base can be given lower probability. The construction of our grammar invokes a novel application of hierarchical Dirichlet processes (HDPs), which in turn, requires a novel and efficient inference approach. We present experimental results showing, for a simple grammar, that our parser outperforms a state-of-the-art CCG semantic parser and scales to knowledge bases with millions of beliefs.

Many real-world relations can be represented by signed networks with positive and negative links, as a result of which signed network analysis has attracted increasing attention from multiple disciplines. With the increasing prevalence of social media networks, signed network analysis has evolved from developing and measuring theories to mining tasks. In this article, we present a review of mining signed networks in the context of social media and discuss some promising research directions and new frontiers. We begin by giving basic concepts and unique properties and principles of signed networks. Then we classify and review tasks of signed network mining with representative algorithms. We also delineate some tasks that have not been extensively studied with formal definitions and also propose research directions to expand the field of signed network mining.