A group of researchers from Nanyang Technological University, Singapore, have created a novel solution for large-scale Android malware detection. It's called DroidOL, and it's an adaptive and scalable malware detection framework based on online learning. "DroidOL's achieves superior accuracy through extracting high quality features from inter-procedural control-flow graphs (ICFGs) of apps, which are known to be robust against evasion and obfuscation techniques adopted by malware," the researchers explained. They used the Weisfeiler-Lehman (WL) graph kernel to extract semantic features from ICFGs, and finally, online learning to distinguish between benign and malicious apps. They attribute much of the success of their technique to the use of a scalable online learning classifier instead of batch-learning classifiers (which are not).

What happens when we teach a computer how to learn? Technologist Jeremy Howard shares some surprising new developments in the fast-moving field of deep learning, a technique that can give computers the ability to learn Chinese, or to recognize objects in photos, or to help think through a medical diagnosis. Get caught up on a field that will change the way the computers around you behave ... sooner than you probably think.

Northwestern Engineering'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 idea that university CS programs are taking bright young minds and fashioning them into algorithm and data structure whiz-kids defies the observations of almost any incoming CS student or their instructor. Many CS freshmen enter college already having a passion for computers and likely a privileged amount of access to technology and mentorship. Like myself, they were given computers as children by parents who were themselves close to technology. They have computer usage skills (how to configure your machine, how to fix basic computer problems) and have parents (or tutors) who introduced them to programming. For those without that background, freshman CS can prove very challenging.

Intel wants to take on Google's Tensor Processing Unit and Nvidia's GPUs in machine learning computing with improvements to its Xeon Phi mega-chips. The company will add new features to Xeon Phi to tune it for machine learning, said Nidhi Chappell, director of machine learning at Intel. Machine learning, a trendy technology, allows software to be trained to do tasks like image recognition or data analysis more efficiently. Intel didn't disclose when the new features will be added, but the next version of Xeon Phi will come by 2018. Intel's already behind chip rivals in machine learning, so it may have to speed up the next Xeon Phi release.

When I was in high school, one of the required classes I had to take was a shop class. I had been taking mostly what the school called "enriched" courses, or what were mostly academic classes that featured primarily reading, writing, and arithmetic. A shop class had more of a trade focus. I was surprised when the first lesson on the first day of my shop class was a richer academic experience than any of the enriched classes I had taken. The instructor started talking about systems, and how many manufacturing processes involved breaking products down into different systems.

Since the application of robotics is largely confined to manufacturing and AI is still in its infancy, the answer now is obviously no. One LinkedIn author recently argued that business schools should be proactive and develop a "Robot MBA" for a future of intelligent non-human workers. Silliness aside, having transformed unskilled jobs, automation is set to disrupt professional life, where MBAs can be a point of differentiation for humans. "The automation we've seen in the last 100 years mostly replaced muscle," says Urs Peyer of Insead. A study by Deloitte and Oxford University found that 35 per cent of current jobs are at high risk of computerisation, with white collar professions among those most under threat.

Students mostly couldn't tell'Jill Watson' wasn't human; 'Yep!' One day in January, Eric Wilson dashed off a message to the teaching assistants for an online course at the Georgia Institute of Technology. "I really feel like I missed the mark in giving the correct amount of feedback," he wrote, pleading to revise an assignment. Thirteen minutes later, the TA responded. "Unfortunately, there is not a way to edit submitted feedback," wrote Jill Watson, one of nine assistants for the 300-plus students. Last week, Mr. Wilson found out he had been seeking guidance from a computer.

Instead of gathering students into a room and teaching them, everybody learns on their own time, on tablets and guided by artificial intelligence. First, I talk to a Ashok Goel, a computer scientist who developed an artificially intelligent TA named Jill Watson and didn't tell any of his students she wasn't a human. Then I talk to two people building future, app based educational systems. Jessie Woolley-Wilson from DreamBox explains what adaptive learning is, and how it can help create a better learning experience for kids. She also talks about all the data they collect on kids to better serve them (data we'll come back to later in the episode.)

It's a cliché to proclaim that technology is having a disruptive influence on our lives, changing our society and the way people work. And, are employees and organisations prepared for the future of work? A recent event – "The Robots are coming: The future of work"- at the South Bank Centre brought together a panel of experts on robotics and artificial intelligence to debate what awaits our working lives in the near future. I was there to explore the topic and report on the talk. Sabine Hauert (pictured right) is a lecturer in robotics and member of Bristol Robotics Laboratory, an academic centre for multi-disciplinary robotics research in the UK.