I've decided that I'm going to get really good at using R. What is R, you might ask? R is a programming language for originally created for statistical analysis, and my interest in it means I've apparently come to terms with the fact that I'm the type of person that jocks gave wedgies and swirlies to in high school. Why am I interested in R? Well, R is a programming language that will enable machine learning. I'm not talking about the robots-flipping-pancakes type of machine learning which -- while cool -- is not what comes to mind when thinking about statistics. Rather, the type of machine learning I'm talking about is where a user programs a computer to learn without being explicitly programmed to do so.

Nowadays, companies such as Google and IBM are doing a great service to all of us by showing what can be achieved by using Artificial Intelligence (AI). For instance, the results achieved by AlphaGo and Watson are outstanding and truly inspiring (the least one can say). But the fact that only companies and labs have access to this technology can represent a threat. First of all, we cannot know how money driven companies are going to use this novel technology. Second, this monopoly slows down technology adoption.

Earlier this week, OpenAI, the nonprofit research group with billion-dollar backing from Elon Musk and other tech luminaries, launched its first program. It's called OpenAI Gym, and it's meant to be used as a benchmarking tool for artificial intelligence programs. Musk once said he thought truly artificial intelligent agents could be more harmful to the human race than nuclear weapons. When OpenAI was launched in December, its stated goal was to "advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return." Which sounds a lot like an AI version of Google's long-held mantra: "Don't be evil."

My secretary, Amy Ingram (AI – geddit?), is designed to help people organise their diaries and set up meetings. That's all she can do. But she goes about it in a freakishly human-like way. In fact, after using her for a couple of weeks, various contacts of mine – after communicating with her over email – said they had no idea she was not a real person. For her to do her job, you need to give her access to your electronic diary, set a few preferences (your three favourite coffee shops to meet in, for example), and copy her into any emails about meetings you want to set up.

This has been the year of the AI machine, and it's been a rapid change. Artificial intelligence has suddenly begun to match and even outperform humans in tasks where we've have always held the upper hand--face recognition, object recognition, language understanding and so on. And yet there are plenty of complex tasks in which AI machines still trail humans. These range from simple housework such as ironing to more advanced tasks such as driving. The reason for the slow progress in these areas is not that intelligent machines can't do these tasks.

A visitor to Mobile World Congress in Barcelona tries on a virtual-reality headset, one of a coming wave of VR devices. SAN FRANCISCO – I love watching people experience virtual reality for the first time. The cumbersome headsets exaggerate their movements as they scan left to right, and then nod up and down. And every time they look in another direction and spot something new, you can almost feel their amazement through the goggles. Lately, I feel as though I've put on a VR headset and never took it off. Because every time I turn, it seems, I spy something truly inspirational.

There are two fundamental milestones I'd say. The first one is Fisher's Linear Discriminant [1], later generalized by Rao [2] to what we know as Linear Discriminant Analysis (LDA). Essentially, LDA is a linear transformation (or projection) technique, which is mainly used for dimensionality reduction (i.e., the objective is to find the k-dimensional feature subspace that -- linearly -- separates the samples from different classes best. Given the objective to maximize class separability, projecting the 2D dataset below onto "x-axis component," would be a better choice than the "y-axis component." Keep in mind though that LDA is a projection technique; the feature axes of your new feature subspace are (almost certainly) different from your original axes.

Those of us who work in data science and artificial intelligence have a love/hate relationship with hype. We're excited by self-driving cars, machines understanding complex images, and computers beating humans at Go (if not StarCraft). On the other hand, we've heard stories of the last'AI winter' and we fear that hype (and the inevitable trough of disillusionment that follows) is setting us up for another one. We know machine learning is math, not magic, and we don't want to be left holding the bag when someone declares that the AI emperor has no clothes. As always, the truth is more nuanced.

Sometime recently the world of healthcare quietly changed and not many people noticed. IBM's artificially intelligent supercomputer Watson decided that it's (his?) prowess at Jeopardy was nothing more than a neat parlour trick and went to med school. Forbes reported that Watson waded through textbooks, medical databases PubMed and Medline and copious quantities of patient records from leading hospitals. "Watson has analysed 605,000 pieces of medical evidence, 2 million pages of text, 25,000 training cases and had the assist of 14,700 clinician hours fine-tuning its decision accuracy," according to Forbes. Supporters now claim that Dr. Watson is now the world's best diagnostician with consistent, accurate diagnoses based on access to essentially all known medical wisdom.

Storm damage repairs and preventative maintenance on power lines and trees are two of the most important tasks utility companies take on, requiring a large amount of time and budget. Currently, ground crews inspect assets manually or via helicopter, and then based on their observations, they'll identify areas that need attention. New technology on the horizon will help protect the grid from potentially dangerous storms and trees that pose a risk of falling. Affordable drone technology, coupled with big data software, is paving the way for a more detailed, holistic approach to storm damage assessment and utility maintenance. Through the collaboration of Edison Electric Institute and Palo Alto-based drone service company Sharper Shape, the EEI Sharper Utility partnership was formed to fast-track long-distance commercial drone inspections of power lines in the U.S. Drone flights have already found their footing in Europe with help from Sharper Shape's European affiliate and are looking to make an impact on the utility industries in the U.S. and the rest of the world.