One of the big breakout manga hits of the past five years or so is definitely Yasuo Ohtagaki's grittier take on the original Mobile Suit Gundam. Set as a sidestory to the main conflict, Gundam Thunderbolt is a fascinating and very different approach to the saga. I was lucky enough to catch up with its author and find out how the manga came about. Considering the huge success of Gundam Thunderbolt, I was curious as to how Ohtagaki had gotten into making manga in the first place. Thankfully, he was more than happy to explain, "I am from Osaka originally and I've really enjoyed manga since I was a child. My father used to buy two volumes of the latest release, although at that time there were already 30 volumes on the market. He would buy the volumes like souvenirs and I would look forward to them. This is how I got into Dokaben and the first thing I copied was the art of Dokaben. I liked manga for a long time but when I was in high school, the romantic comedy boom arrived. I started to look at Akira Oze sensei's work, as he was at the time drawing romantic comedy manga. I realized then that I wanted to create manga like Oze sensei. "At the time when I was drawing manga, there was the romantic comedy boom and I liked this type of manga, the kind where a boy and girl flirt.

One of the theoretical advantages of software, artificial intelligence, algorithms, and robots is that they don't suffer many human foibles. They don't get sick or tired. They are polite -- or rude -- to everyone in equal measure. The reality, of course, is different. Technology is designed by humans in all their frailty. As a result, it is eminently capable of perfect human behavior.

Machine learning (ML) technologies and solutions are expected to become a prominent feature of the information security landscape, as both attackers and defenders turn to artificial intelligence to achieve their goals. "The advent of machine learning in security comes alongside the increased capability for collecting and analyzing massive datasets on user behavior, client characteristics, network communications, and more. As we have already witnessed in many other technological domains, I think machine learning will become the main driver for innovation in information security in the coming decade," says security researcher Clarence Chio. Alongside Anto Joseph, a security engineer at Intel, Chio is scheduled to give Hack In The Box attendees a quick and practical introduction to the world of machine learning in April. But, he says in advance, machine learning is no silver bullet.

Phillip De Leon has been named associate dean of research and doctoral studies for the New Mexico State University College of Engineering. LAS CRUCES -- Through the use of New Mexico State University's High Performance Computing system, a supercomputer known as Joker, Phillip De Leon, associate dean for research in the College of Engineering, not only conducted research but students in his graduate electrical engineering course also used the system. In the Pattern Recognition and Machine Learning course, which is a data science class De Leon taught in the fall, graduate students used Joker on projects that included developing machine learning codes and evaluating the models with standard datasets. "These projects including identifying a song much like the Shazam app, recognizing handwritten digits like ZIP codes, classifying email as ham or spam, analyzing Twitter feeds, etc.," De Leon said. "Being able to use this system allowed the students to experiment and tune their codes much faster since everything ran much faster. It also allowed for big datasets to be used in training and evaluation."

In this article, I present a few modern techniques that have been used in various business contexts, comparing performance with traditional methods. The advanced techniques in question are math-free, innovative, efficiently process large amounts of unstructured data, and are robust and scalable. Implementations in Python, R, Julia and Perl are provided, but here we focus on an Excel version that does not even require any Excel macros, coding, plug-ins, or anything other than the most basic version of Excel. It is actually easily implemented in standard, basic SQL too, and we invite readers to work on an SQL version. In short, we offer here an Excel template for machine learning and statistical computing, and it is quite powerful for an Excel spreadsheet.

Robots are interacting with patients in medical facilities, handling material in warehouses, working in manufacturing, and helping ill children attend school--all with a hand from the Cloud. Indeed, by linking to the Cloud, robots are bringing homebound students into classrooms, hallways and cafeterias to socialize with their friends and continue learning--in school. "When you allow the student to actually be there, move around, go to classes and go to recess, you return a sense of control," said Daniel Theobald, chief innovation officer and co-founder of Vecna, a Massachusetts-based company that has created the VGo Robotic Telepresence. The VGo robot is essentially a virtual student who is present in the classroom and interacts in all the usual ways, even able to raise a hand (so to speak) to respond to questions in real time. But its creators hope you won't think of it as just a fancy Skype or FaceTime.

A new brain-computer interface developed by scientists can read a person's thoughts in real time to identify when a robot makes a mistake, an advance that may lead to safer self-driving cars. Most existing brain-computer interface (BCI) require people to train with it and even learn to modulate their thoughts to help the machine understand, researchers said. By relying on brain signals called "error-related potentials" (ErrPs) that occur automatically when humans make a mistake or spot someone else making one, the new approach allows even complete novices to control a robot with their minds. This technology developed by researchers at the Boston University and the Massachusetts Institute of Technology (MIT) may offer intuitive and instantaneous ways of communicating with machines, for applications as diverse as supervising factory robots to controlling robotic prostheses. "When humans and robots work together, you basically have to learn the language of the robot, learn a new way to communicate with it, adapt to its interface," said Joseph DelPreto, a PhD candidate at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL).

CHENNAI: With automation and artificial intelligence emerging in several sectors, more prominently in the IT sector, employees are looking at upskilling' or re-skilling themselves by gaining new skills such as Android Development, Machine Learning, and so on. This is directly reflecting on the kind of courses people are searching for and enrolling in. Udacity, a US-based online educational platform, recently released a survey that reflects on the kind of interest being shown in online courses. In Chennai, some of the popular online courses include those that involve data analysis, deep learning, android development, machine learning, and frontend web developer jobs. The platform which carries out online learning found that the three most popular courses in the country which saw the highest de mand were Android Development, Machine Learning and Deep Learning.

I am giving a talk (in French) at the 85th edition of the ACFAS congress, May 9. I will discuss the engineering aspects of doing machine learning. But more importantly, I will discuss how Semantic Web techniques, technologies and specifications can help solving the engineering problems and how they can be leveraged and integrated in a machine learning workflow. The focus of my talk is based on my work in the field of the semantic web in the last 15 years and my more recent work creating the KBpedia Knowledge Graph at Cognonto and how they influenced our work to develop different machine learning solutions to integrate data, to extend knowledge structure, to tag and disambiguate concepts and entities in corpuses of texts, etc. One thing we experienced is that most of the work involved in such project is not directly related to machine learning problems (or at least related to the usage of machine learning algorithms). And then I recently read a survey conducted by CrowdFlower in 2016 that support what we experienced.

As marketers strive to engage in more meaningful conversations with their audience, understanding which words, phrases, sentences and even content formats resonate with particular audience members is key. Last year we saw progress in lexical analysis with the goal of finding content or text that drove overall marketing success. It did this by analyzing successful campaign content versus unsuccessful content. I believe 2017 will see that work get personalized by combining content analysis at the campaign level with content analysis at the individual level. The interconnected data makes it possible.