NIIT, a global leader in skills and talent development, today launched a course in Web App Development with MEAN Stack under the DigiNxt Series. The company has recently ventured into Digital Transformation to offer pioneering programs to young aspirants wishing to enter the digital services industry, as well as to IT professionals wishing to reskill themselves for the new digital world. The cutting-edge program will use the student-centred pedagogy of project-based learning to help them carve a successful career in the emerging digital era. Some of the famous web applications like LinkedIn, Netflix, Uber, Paypal, etc. have been built using MEAN Stack. AngularJS, Node.js (MEAN) represents a group of open source technologies which are known to synergize well together, thereby empowering students to launch their own web and mobile apps.

A school in London is set to become the first in Britain to welcome a robot teacher when it opens in September. The humanoid robot, known as Pepper, will be used in classrooms at the London Design and Engineering University Technical College, to help teach pupils about cutting-edge robotics. It will be the first instance of an educational robot being used in a UK classroom. Claims made by an expert in artificial intelligence predict that in less than five years, office jobs will disappear completely to the point where machines will replace humans. The idea that robots will one day be able to do all low-skilled jobs is not new, but Andrew Anderson from UK artificial intelligence company, Celaton, said the pace of advance is much faster than originally thought.

The Amazon Echo is an unlikely hit. After all, the world's largest online retailer hasn't always won its bets on hardware. And a gadget that relies solely on voice? Yet Amazon has by one estimate sold some 3 million of the squat cylinders since the Echo launched in November, 2014. The company doesn't share sales data, but it did say Alexa, the voice-activated software that powers Echo, is active in millions of places, including smartphone apps and other Amazon gadgets.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

We present three case studies of organizations using a data science competition to answer a pressing question. The first is in education where a nonprofit that creates smart school budgets wanted to automatically tag budget line items. The second is in public health, where a low-cost, nonprofit women's health care provider wanted to understand the effect of demographic and behavioral questions on predicting which services a woman would need. The third and final example is in government innovation: using online restaurant reviews from Yelp, competitors built models to forecast which restaurants were most likely to have hygiene violations when visited by health inspectors. Finally, we reflect on the unique benefits of the open, public competition model.

IN JULY 2011 Sebastian Thrun, who among other things is a professor at Stanford, posted a short video on YouTube, announcing that he and a colleague, Peter Norvig, were making their "Introduction to Artificial Intelligence" course available free online. By the time the course began in October, 160,000 people in 190 countries had signed up for it. At the same time Andrew Ng, also a Stanford professor, made one of his courses, on machine learning, available free online, for which 100,000 people enrolled. Both courses ran for ten weeks. Such online courses, with short video lectures, discussion boards for students and systems to grade their coursework automatically, became known as Massive Open Online Courses (MOOCs).

EXPERTS warn that "the substitution of machinery for human labour" may "render the population redundant". They worry that "the discovery of this mighty power" has come "before we knew how to employ it rightly". Such fears are expressed today by those who worry that advances in artificial intelligence (AI) could destroy millions of jobs and pose a "Terminator"-style threat to humanity. But these are in fact the words of commentators discussing mechanisation and steam power two centuries ago. Back then the controversy over the dangers posed by machines was known as the "machinery question".

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Northwestern University's Ken Forbus is closing the gap between humans and machines. Using cognitive science theories, Forbus and his collaborators have developed a model that could give computers the ability to reason more like humans and even make moral decisions. Called the structure-mapping engine (SME), the new model is capable of analogical problem solving, including capturing the way humans spontaneously use analogies between situations to solve moral dilemmas. "In terms of thinking like humans, analogies are where it's at," said Forbus, Walter P. Murphy Professor of Electrical Engineering and Computer Science in Northwestern's McCormick School of Engineering. "Humans use relational statements fluidly to describe things, solve problems, indicate causality, and weigh moral dilemmas."

The most important subpackages provide implementations of boilerplate code that is relevant to machine-learning problems. These include computer vision, natural language processing, and speech processing. Other subpackages may be smaller and focus on more specific problems or even specific data sets.