The next year will see robots begin to join forces to collaborate in unprecedented new ways, experts have predicted. British academics have predicted the rise of a global system called "the internet of robots" which will let machines interact and communicate on an international scale. Although robots are currently too primitive to pose any major threat to humanity, the development of new forms of communication marks the beginning of a world where machines begin to teach each other how to perform tasks and share their knowledge across "cloud" computer systems which can be accessed from anywhere on Earth. Tom Garner, a research fellow at Portsmouth University's School of Creative Technologies, said: "These systems allow robots that have been optimized for different tasks to work on specific problems individually, but to pass solutions between each other." "The robots use the cloud to share the data, enabling it to be analyzed by any other robot or intelligence system also connected to the same network."

Back in 2008, Yair Lavi founded Tonara, an interactive app that "listens to" musicians and assists them as they play. He has transported the lessons learned there to his latest venture, 3DSignals, where the co-founder and head of algorithms uses ultrasonic sensors and deep-learning software to detect anomalies in machine sounds. "Industrial music" you might say. Smart Industry: How do you define "deep learning"? Yair: Deep learning is a method of artificial intelligence used to detect patterns in data, either independently or based on some type of training.

Enterprises today are finding it exceedingly meaningful and resourceful in the massive amounts of data they generate and save every day. The required algorithms, applications and frameworks to bring greater predictive accuracy and value to enterprises' data sets are available; therefore, businesses need to make sure they have data sets of sufficient size and quality. It is due to the excessive need to do a better job in capturing and utilizing data. The rise of deep learning and neural networks has spread in everyday lives. It took about six years for neural nets to show impressive results, first in speech recognition, then computer vision, images, image detection and diagnostics, and more recently, in natural language processing.

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.