Soteria Intelligence is excited to announce the company's research and development focused on creating the next generation of social media threat intelligence solutions with the power to learn and become more effective over time. Instead of taking an approach that looks at combinations of keywords, conversations on a map or other social media analytics that produce a high number of recurring false positives, Soteria Intelligence is implementing machine learning to use false positives, vetted by analysts, as educational tools that continually improve system accuracy. The company is also training its system with proprietary data on past social media threats and, through the understanding of false positives and integration of historical data, combined with natural language processing, image recognition, sentiment analysis and other functionality, Soteria Intelligence is revolutionizing the way social media threats are identified and assessed. "As someone who has spent the past 10 years guiding large organizations throughout the world on complex social media initiatives while using social media analytics tools along the way, I understand where they shine and fall short. I also see what's needed to accurately identify threatening social media behavior on the horizon," stated Aaron Schoenberger, Founder and CEO of Soteria Intelligence.

Thousands of openings in artificial intelligence and machine learning posted on job boards are going unfilled. In fact, though AI is one of the fastest-growing areas for high-tech professionals, according to a recent Kiplinger report, there are too few qualified engineers. "Supply is far lower than demand," says Boris Babenko, a machine vision engineer at Orbital Insight, a company in Palo Alto, Calif., that uses AI to make sense of data gathered from satellite images. "That's true of all software engineering, but AI is a niche on top of that." The need for AI specialists exists in just about every field as companies seek to give computers the ability to think, learn, and adapt.

"How many robots can I control with this? That really is a robot army." Despite how it sounds, world domination isn't on the agenda. There are only two robots in the room, and right now they're more concerned with drawing neat triangles on pieces of paper, rather than enslaving humanity. The pair of Mirobots are demonstrating Project Bloks, Google's latest attempt to encourage children to learn programming skills.

The move to analogies as teaching methods is a major step in "cognitive computing," which allows computers to learn and even reprogram themselves. Cognitive computing is the big evolutionary step to true artificial intelligence. The new research is being carried out by Northwestern University, using a new approach called the Structure Mapping Engine (SME) which is capable of analogic problem solving, including "moral dilemmas". So far, the results are pretty straightforward -- computers retrieve memories to find analogic situations -- Case A resembles Example C, but not Cases B or D, etc. This type of very basic, very important learning is roughly kindergarten level for humans.

If you're interested in large scale machine learning, then this book is for you. This book doesn't provide deep coverage of theoretical foundations of machine learning (I would recommend to look to other books, like Introduction to Machine Learning (Adaptive Computation and Machine Learning series), Machine Learning in Action or Programming Collective Intelligence: Building Smart Web 2.0 Applications, etc., if you want to get more background), but concentrates on explanation on how to use Apache Mahout ([...]) to solve some of machine learning problems: making recommendations, data clustering & classification. For each of class of these problems, description starts with base things, and continues with more complex examples, including complete solutions, that could be easily adapted for your machine learning problems. All examples that come with book were checked with actual release of Apache Mahout (version 0.5). Book is written in succinct, but understandable language and provides many code snippets that make understanding of topics much easier.

In the daily edtech trenches, the forest is easily lost for the trees. Technological minutiae in the classroom carry such immense consequences that it can be hard to think beyond tomorrow's software update, nevermind next year's LMS rollout. In his opening keynote at the ISTE 2016 conference, noted physicist Dr. Michio Kaku showed educators the forest that he and others believe will encircle the classroom of the future. And oh, what a forest it might be. According to Dr. Kaku, talking wallpaper, data-reading toilets and other technologies that seem like miracles today are a mere fifty years away.

For the past six years, I've been developing Python Tutor (pythontutor.com), Thousands of people use it every day to run tens of thousands of pieces of code in seven languages: Python, Java, JavaScript, TypeScript, Ruby, C, and C . This tool has also become a platform for HCI, educational technology, and computing education research. Most recently, it formed the basis for my faculty job applications that got me a job at UC San Diego. How did this project grow from nothing to its current state? I've been wanting to write a "history of Python Tutor" article for a while now but never found a good time to do so.

If a change is needed, it is of a different kind. The process through which students may petition for loan forgiveness may need to be streamlined and made clearer. If it acts at all, the Department of Education should redefine the current process and better communicate its availability to students. The new initiative, even if that is not its intention, has the potential to go too far because those who feed off society's productive activities – because that's where the money is – will take it there. They will find ways to expand on language so vague its inevitably loose interpretation leaves public and private universities vulnerable to countless claims that are without merit.

Engineers have developed a robotic shopping trolley for elderly customers based on a drawing by a boy who simply wanted to help his grandmother. Aidan McCann, 13, dreamed up a push cart with height adjustment features to help his grandmother Lydia who'isn't very strong'. He witnessed how the 4ft 11in 76-year-old finds it difficult to carry groceries from the shops and perform other physically demanding tasks due to her height. Bosses at engineering giant Doosan Babcock were so impressed with Aidan's design they selected it as their overall winner at the Scottish Engineering Special Leaders Award 2015 Users can make the trolley go up and down by the flick of a switch. The idea is to lift bags of shopping towards the users so that they don't have to bend down and lift it up themselves.

IBM's Watson is still in its early days, but the cognitive computing system could end up having a substantial impact on a number of industries, particularly healthcare and education. For example, Alder Hey Children's Hospital in England is currently using the technology to improve the patient experience, while an interactive toy called the Cognitoys Dino uses Watson to answer a child's questions in a kid-friendly and personalized way. Another space that could largely benefit from Watson's capabilities is the gaming industry. The interactive nature of games paired with Watson's natural language processing capabilities and data analysis has already led to a number of new gaming initiatives, including the first-ever Minecraft game that utilizes Watson. Called'Medical Minecraft,' the game was recently created by a group of high school students.