Set to become a $165 Billion dollar industry by 2020, there's still a common question that lingers among many newcomers trying to understand this fast moving digital phenomena we are just beginning to watch evolve; What's the difference between them and how will it impact the digital world as I currently know it? Before we jump into the mind-blowing future Mixed Reality is set to usher in over the course of 2017, let's first discuss the distinctions between Virtual & Augmented Reality – Their technologies are very similar but have some fundamental differences. Virtual Reality is a digital environment that shuts out the real world. VR is able to transpose the user. In other words, bring us someplace else.

Apple was the first to introduce a digital assistant when it acquired Siri and baked it into the OS. The makers of Siri went on to create Viv, a realisation of their original vision for Siri. Viv was acquired by Samsung this year. Despite the initial head start with Siri, Apple lost ground over the subsequent years by being very secretive about its research. As Apple employees were not allowed to publish research, the best talent was not attracted to the company.

Sami Haddadin once attached a knife to a robot manipulator and programmed it to impale his arm. He was demonstrating how a new force-sensing control scheme he designed was able to detect the contact and instantly stop the robot, as it did. Now Haddadin wants to make that same kind of safety feature, which has long been limited to highly sophisticated and expensive systems, affordable to anyone using robots around people. Sometime in 2017, his Munich-based startup, Franka Emika, will start shipping a rather remarkable robotic arm. It's designed to be easy to set up and program, which is nice.

According to John Schroeder, executive chairman and founder of MapR Technologies, Inc., the acceleration in big data deployments has shifted the focus to the value of the data. We covered MapR's big data advancements several times this year, and the folks at MapR recenty shared Schroeder's six major predictions for the technology market in general in 2017. In the 1960s, Ray Solomonoff laid the foundations of a mathematical theory of AI, introducing universal Bayesian methods for inductive inference and prediction. In 1980 the First National Conference of the American Association for Artificial Intelligence (AAAI) was held at Stanford and marked the application of theories in software. AI is now back in mainstream discussions and the umbrella buzzword for machine intelligence, machine learning, neural networks, and cognitive computing.

Being a scientist can be like willingly entering into a Roman gladiatorial contest. The hours are long, there's a rank smell of indentured servitude, and at any minute your colleagues may attempt to eviscerate you for the pleasure of the crowds. A lot of the time we can look beyond these challenges because we have an innate need to explore our curiosity. Or perhaps (shockingly) because we feel that a life spent in pursuit of knowledge is still a noble and useful thing. At other times I suspect we only stick around because we're playing the real-world equivalent of a video game--conditioned to crave the chemical release from a momentary discovery, or a satisfactory fitting of a curve to data points.

Regressions are widely used to estimate relations between variables or predict future values for a certain dataset. If you want to know how much of variable "x" interferes with variable "y" you might want to do a regression in your data. If you have a bunch of data points in time, and you want to know what is your data going to look like in the future, you also might want to do regression. I will try to describe the steps that helped me successfully build linear and non-linear regression in R, using polynomials and splines. I am not going to go on too much details on each method.

Machine learning and other variations of artificial intelligence (AI) are expected to proliferate in the enterprise in 2017. The majority of IT players, including today's leading technology companies, have invested in the space and plan to increase efforts for the foreseeable future, according to analysts who cover the market. However, while machine learning has generated tremendous interest throughout the enterprise, a wide gap still exists between research and beneficial use cases in the real world. And only a small number of companies have the resources to actually drive AI innovation and deliver it to the masses, sources say. During last month's Code Enterprise conference, LinkedIn CEO Jeff Weiner said AI was one of leading factors in the company's decision to be acquired by Microsoft.

I was reminded of a mathematical hypothesis called the singularity when I read Vinod Khosla's recent interview in the Wall Street Journal and his prediction of massive job displacement and the growth of new industries due to the widespread adoption of artificial intelligence (AI). The singularity is a point and phase in the future when bio, nano, energy, robotic, and computer technology will develop at such a rate, become so advanced, and have such a profound impact on humanity, that today's society has no means to understand or describe what life will be like at that time in the future. It made me wonder how far and fast we are heading in the same explosion of unfathomable change occurring today in information security. Just as IT revolutionized all forms of business in the last half-century, and the Internet in turn revolutionized IT in the last quarter-century, the trajectory we are on now places AI squarely at the next technology inflection point. The study of history often provides a strong predictor of human societal change.

Machine Learning is a vast area of Computer Science that is concerned with designing algorithms which form good models of the world around us (the data coming from the world around us). Within Machine Learning many tasks are - or can be reformulated as - classification tasks. In classification tasks we are trying to produce a model which can give the correlation between the input data and the class each input belongs to. This model is formed with the feature-values of the input-data. For example, the dataset contains datapoints belonging to the classes Apples, Pears and Oranges and based on the features of the datapoints (weight, color, size etc) we are trying to predict the class. We need some amount of training data to train the Classifier, i.e. form a correct model of the data.

Our smartphone currently represents the most expensive area to be purchased per squared centimeter (even more expensive than the square meters price of houses in Beverly Hills), and it is not hard to envision that having a bot as unique interfaces will make this area worth almost zero. None of these would be possible though without heavily investing in speech recognition research. Deep Reinforcement Learning (DFL) has been the boss in town for the past few years and it has been fed by human feedbacks. However, I personally believe that soon we will move toward a B2B (bot-to-bot) training for a very simple reason: the reward structure. Humans spend time training their bots if they are enough compensated for their effort.