When Intel announced that it would lay off 12,000 workers in an effort to restructure itself for the post-PC age, there was one notable word missing from its spin-heavy press release: mobile. The press release said that the chipmaker was cutting 11 percent of its global workforce in order to "accelerate its evolution from a PC company to one that powers the cloud and billions of smart, connected computing devices." But that doesn't mean smartphones--or, at least, it doesn't seem to. The bit about "smart, connected computing devices" is a reference to the Internet of Things. Okay, that gawd awful buzz-phrase don't really capture the full scope of what Intel is trying to do there--it's not just Internet-connected thermostats.

With the next series of Game of Thrones set to hit our screens this month anxious fans may be wondering which one of their favourite characters is next on the hit list. The first five of the series have not been an easy watch, with the writers killing off key characters just when their luck starts to change and as audiences warmed to them. But researchers have developed a computer algorithm aimed at predicting the next character to die in the hit series. Students in Germany have developed a GoT-related computer algorithm which uses available data from the internet to predict the next character to die. By trawling the internet for data and clues, a team of computer scientists have created a model which crunches the numbers to work out which characters are most likely to die in the upcoming sixth series.

Tell us more about your favorate machine learning framework in comments. Bio: Devendra Desale(@DevendraDesale) is a data science graduate student currently working on text mining and big data technologies. He is also interested in enterprise architectures and data-driven business. When away from the computer, he also enjoys attending meetups and venturing into the unknown.

MIT researchers have announced that they've concocted a new artificial intelligence system capable of successfully detecting 85% of cyber-attacks. The AI2 platform, produced by the MIT's Computer Science and Artificial Intelligence Laboratory (in conjunction with PatternEx, a machine learning startup), has notched up a much better record than previous systems. The 85% accuracy rating is three times better than previous benchmarks which have been recorded, and it also produced far less false positives, in fact a reduction of a factor of five was observed. MIT notes that AI2's initial testing ran over a period of three months and involved combing through some 3.6 billion log lines looking for suspicious activity, using machine learning to make the initial detections and then putting those in front of a human security analyst who confirmed whether or not a detection was an actual cyber-attack. AI2 then learned from that feedback, improving its routines for the next round of detection. Essentially, the system utilises the best in artificial intelligence smarts combined with human error correction which feeds the machine learning process, and AI2 is apparently capable of honing itself rapidly indeed.

Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have collaborated alongside the machine learning startup PatternEx to demonstrate how an artificial intelligence platform that makes use of continuous input from human experts would be able to predict cyber attacks better than the systems that exist today. CSAIL and PatternEx are calling their new AI platform AI² due to how it combines the intuition of analysts with artificial intelligence. Previously'analyst-driven solutions' relied on the rules created by security experts which resulted in attacks that did not correspond to their rules slipping through the cracks. The machine learning approach to cyber-security relies on'anomaly detection' to discover possible attacks but often results in false positives. CSAIL and PatternEx created AI² by combining machine learning's ability to detect anomalies with the knowledge of analysts to flag possible threats.

An engineer recently asked me how she could turn an interest in machine learning into a full-time job. This can be a daunting prospect, because the whole field has until recently been very separate from traditional engineering, with only a few specialists at large companies using it in production, often far from traditional product teams. I took a very random path to focusing on deep learning full time, but so did most of the people I work with. It's not clear that there is one good route, but I wanted to share the advice I had to offer in case it's helpful to others. Every manager should point at one member of their team and say "You are now our machine learning expert".

In the busy weeks leading up to RSA this year, I was taking a rare break to drive my daughter to the airport. She was flying back to school to continue her 2nd year at University of Toronto (shout out to all of my Canadian peeps!). Btw, if you've not seen "Stronger Beer" highly recommended. Anyway, my daughter asked me an intriguing question on the ride to LAX. She said, "Last time I got caught in a random search… do you think the TSA finds anything doing that…" Great question, and my answer was "No" it's a horrible way to search people.

Have you ever joked that you wished you could clone yourself? Well, it looks like if you're an extremist of any stripe who spends a lot of time on social media, you'll soon be able to fulfill that dream. Swarms of real life, human trolls have already been able to achieve some remarkable things. For example, there's the well-known incident where Time's Man of the Year Poll met 4chan. But real-life trolls have to sleep.

Leave a human baby with some toys and it'll quickly learn to pick them up. Now a robot with deep-learning capabilities has done the same thing.