Last March, Google's computers roundly beat the world-class Go champion Lee Sedol, marking a milestone in artificial intelligence. The winning computer program, created by researchers at Google DeepMind in London, used an artificial neural network that took advantage of what's known as deep learning, a strategy by which neural networks involving many layers of processing are configured in an automated fashion to solve the problem at hand. Unknown to the public at the time was that Google had an ace up its sleeve. You see, the computers Google used to defeat Sedol contained special-purpose hardware--a computer card Google calls its Tensor Processing Unit. Norm Jouppi, a hardware engineer at Google, announced the existence of the Tensor Processing Unit two months after the Go match, explaining in a blog post that Google had been outfitting its data centers with these new accelerator cards for more than a year.

It's been a year of change in the enterprise software market. SaaS providers are fighting to compete with one another, machine learning is becoming a reality for businesses at a larger scale, and containers are growing in popularity. Here are some of the top trends from 2016 that we'll likely still be talking about next year. As more and more companies adopt software-as-a-service products like Office 365, Slack, and Box, there is increasing pressure to collaborate for companies that compete with each another. After all, nobody wants to be stuck using a service that doesn't work with the other critical systems they have.

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.