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.

Below is what happened in search today, as reported on Search Engine Land and from other places across the web. Can machine learning be applied to your PPC accounts to make them more efficient? Columnist David Fothergill describes how he utilized machine learning to find new keywords for his campaigns. Inadvertently ruining your newly redesigned website's SEO can be a nightmare. Columnist Will Scott explains four mistakes you must avoid before you launch.

A 2014 Gallup Poll puts a positive spin on the rise of Americans' satisfaction with our education system stating that 48% of Americans are satisfied. This is not something to be celebrated. Our education system needs an extensive overhaul. The standards put on all students do not measure a student's true knowledge. The type of testing mandated does not test to students' abilities but rather how well they know how to take a test. Teachers are leaving the profession in droves due to the immense work load, low pay, lack of support and insurmountable requirements to advance student performance.

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.