Boosting algorithms have been widely used to tackle a plethora of problems. In the last few years, a lot of approaches have been proposed to provide standard AdaBoost with cost-sensitive capabilities, each with a different focus. However, for the researcher, these algorithms shape a tangled set with diffuse differences and properties, lacking a unifying analysis to jointly compare, classify, evaluate and discuss those approaches on a common basis. In this series of two papers we aim to revisit the various proposals, both from theoretical (Part I) and practical (Part II) perspectives, in order to analyze their specific properties and behavior, with the final goal of identifying the algorithm providing the best and soundest results.

Least Squares Twin Support Vector Machine (LSTSVM) is an extremely efficient and fast version of SVM algorithm for binary classification. LSTSVM combines the idea of Least Squares SVM and Twin SVM in which two non-parallel hyperplanes are found by solving two systems of linear equations. Although the algorithm is very fast and efficient in many classification tasks, it is unable to cope with two features of real-world problems. First, in many real-world classification problems, it is almost impossible to assign data points to a single class. Second, data points in real-world problems may have different importance. In this study, we propose a novel version of LSTSVM based on fuzzy concepts to deal with these two characteristics of real-world data. The algorithm is called Fuzzy LSTSVM (FLSTSVM) which provides more flexibility than the binary classification of LSTSVM. Two models are proposed for the algorithm. In the first model, a fuzzy membership value is assigned to each data point and the hyperplanes are optimized based on these fuzzy samples. In the second model we construct fuzzy hyperplanes to classify data. Finally, we apply our proposed FLSTSVM to an artificial as well as three real-world datasets. Results demonstrate that FLSTSVM obtains better performance than SVM and LSTSVM.

While only about a quarter of surveyed business executives say they're currently using artificial intelligence in the workplace to automate manual tasks, a vast majority of those who said they weren't using AI actually were without realizing it. The National Business Research Institute in conjunction with Narrative Science, an AI technology company, surveyed more than 230 senior US business and technology executives in April and May to learn how they use AI-related technologies in their organizations. About 26% of respondents acknowledged using AI systems to automate repetitive tasks, up from 15% surveyed last year. But 88% of those who denied using AI solutions reported that they rely on services or products powered by AI techniques. These include predictive analytics, automated written reporting and communications, and voice recognition and response.

Think of artificial intelligence, and your brain might recall Will Smith's misadventures in I, Robot. But in reality, AI-powered apps are going to prove themselves useful--even indispensable--in the near-future. The technology at the heart of AI is machine learning--the ability for programs to teach themselves new tricks. The Complete Machine Learning Bundle is aimed at aspiring developers who want to get ahead of the curve. With 10 courses and 63.5 hours of video tutorials, it offers an impressive lineup of content.

Just a few short years ago, artificial intelligence was seen as nothing more than a villain in sci-fi movies. This revolutionary technology was used to insist that innovating too fast can lead to a future of robot overlords and murderous machines. From Hal 9000 in 2001: A Space Odyssey to Skynet in Terminator, artificial intelligence was in need of a good public relations person to change it's image. Fortunately, the tech community has been happy to oblige and AI technology has taken off like a self-landing rocket. According to data from CB Insights, investments in artificial intelligence have been through the roof in recent years. While investments hovered around 700 million in 2013, they have reached astronomical levels at nearly 2.4 billion in just a few short years.

There's a small but growing handful of robotics companies trying to make it in the warehouse market with systems that work with humans on order fulfillment. Generally, we're talking about clever wheeled platforms that can autonomously deliver goods from one place to another, while humans continue do the most challenging part: picking items off of shelves. There's a lot of value here, since using robots to move stuff frees up humans to spend more of their time picking. Ideally, however, you'd have the robot doing the picking as well, but this is a very difficult problem in terms of sensing, motion planning, and manipulation. And getting a robot do pick reliably at a speed that could make it a viable human replacement is more difficult still.

MedyMatch Technology Ltd., announced today that it has been ranked in the list of "100 Most Promising BigData Solution Providers" by CIOReview. "The companies selected for our 100 Most Promising BigData Solution Providers 2016 list are an elite group of companies whose products and solutions are changing their respective industries," said Jeevan George, Managing Editor of CIOReview. "We are proud to feature MedyMatch Technology in this edition for its effort in helping organizations to easily and quickly adopt BigData analytics as a core part of their business and accelerate conversion of data into valuable business insights." "It is an honor to be recognized by CIOReview for MedyMatch's achievements in cognitive analytics, artificial intelligence and medical imaging," said Robert Mehler, coFounder & COO. "It is a testament to the accomplishments and capability of our product development team in conjunction with our medical big data clinical partnerships," adds Mehler.

As the pioneer in artificial intelligence field, Baidu chose the Inspur NF5568M4 heterogeneous supercomputing server in its unmanned auto road condition model training. Artificial intelligence has advanced through the years and voice recognition, intelligent hardware, and driverless cars are all technologies that influence our lives. Behind artificial intelligence technology is a neural network that is built from deep learning -- mimicking mechanisms of the human brain when interpreting data. In order to meet all the latest deep learning requirements, a high-performance CPU GPU co-processing acceleration server is growing to become the essential foundation for artificial intelligence hardware.

The author reflects on new research reports on the impact of artificial intelligence (AI) on the workplace. His prediction: AI will make internal politics nastier within management ranks. Artificial intelligence is a hot topic these days. Not surprisingly, news outlets north of the 49th parallel jumped on the release of a new research report cleverly titled "The Talented Mr. Robot: The Impact of Automation on Canada's Workforce." The study was conducted by the Brookfield Institute for Innovation Entrepreneurship, a newly created independent and nonpartisan institute, housed within the Ryerson University of Toronto, dedicated to advancing Canada's innovation and entrepreneurship.

Analytics, statistics, operations research, data science and machine learning - with which term do you prefer associate? Are you from the House of Capulet or Montague, or do you even care? That which we call a rose By any other name would smell as sweet." Romeo was from the house of Montague, Juliet, the house of Capulet, and this distinction that meant that their families were sworn enemies. The play is a tragedy, because by the end the two lovers end up dead as a result of this long-running feud. Statistics, data science and machine learning are but a few of the "houses" that feud today over names, and while to my knowledge no deaths have resulted from this debate the competing camps have nearly come to blows. How has the emerging field of Analytics impacted the Operations Research Profession? Is Analytics part of OR or the other way around? Is it good, bad, relevant, a nuisance or an opportunity for the OR profession? Is OR just Prescriptive or is it something more? In this panel discussion, we will explore these topics in a session with some of the leading thinkers in both OR and Analytics. Be sure to attend to have your questions answered on these highly complementary and valuable fields."