"Fake news" vexed the media classes greatly in 2016, but the tech world perfected the art long ago. With "the internet" no longer a credible vehicle for Silicon Valley's wild fantasies and intellectual bullying of other industries – the internet clearly isn't working for people – "AI" has taken its place. Almost everything you read about AI is fake news. The AI coverage comes from a media willing itself into a mind of a three year old child, in order to be impressed. For example, how many human jobs did AI replace in 2016?

In the passing year many breakthroughts of Artificial Intelligence (AI) occurred. But right now the technology industry reaches a new step of evolution: Using Artificial Intelligence - or better Machine Learning (ML) - to advance and enhance Cyber Security. As Ken Levine - an american (video) game developer - mentioned: a human hacker will one day not be able to defeat an AI, because it won't make any mistakes. So why don't use AI to improve the Cyber Security, which is extremely needed in times of the Internet of Things (IoT) and Financial Technology (FinTech)? Google, as one of the big companies in the race of AI, made an experiment in which two Artificial Intelligences had to communicate in a secure way, while a third one had to decrypt what they encrypt.

While "big data" can be a misunderstood buzzword in tech, there's no denying that the recent AI and machine learning push is dependent on the labeling and synthesis of huge amounts of training data. A new trend report by advisory firm Ovum predicts that the big data market--currently at $1.7 billion--will swell to $9.4 billion by 2020. So what do data insiders see happening in the coming year? TechRepublic spoke to several leaders in this field to find out. Here are five big data trends to watch in 2017, from the experts.

Predictions are ultimately a fool's game. While it's certainly possible to make some broad assumptions about the next 12 months in technology (Apple will release a new iPhone! More firms will introduce cloud services!), the fast-changing nature of the industry can quickly wreck even the most seemingly surefire trends. With that in mind, here are some cautious forecasts about the hottest technologies of 2017. Sure, we could be wrong.

Just as Skype revolutionized the way we communicate, and TransferWise found a way to break worn patterns in the financial world, Estonia is about to make a transforming difference in the dynamic field of robotics too. Besides the country being a Mecca for data and tech-driven startups, more and more Estonians, in addition to excelling in code writing and software development, are thrusting their hands deep into the wiring and metal of robots. Imagine a vacuum-cleaner-sized, yellow'turtle' diving elegantly down hundreds of metres to probe ship wrecks, thus gathering valuable data for archaeologists and maybe even finding lost treasures buried somewhere deep under the sea. But this intelligent'turtle' is actually a robot. Equipped with a camera and a searchlight, the relatively cheap and highly manoeuvrable U-CAT (Underwater Curious Archaeology Turtle) locomotion principle is similar to that of the sea turtles of the natural world.

As AI integration becomes more prominent, one can't help but to think about just how intelligent deep learning technology will be in the future. One of the first place many of our minds go is to AI becoming too intelligent and taking matters into its own virtual hands. How accurate are those portrayals, though? Will it get to a point where we're overpowered by AI, to the point where we're under their metaphorical thumb? TNW Conference is back for its 12th year.

Last year was huge for advancements in artificial intelligence and machine learning. But 2017 may well deliver even more. Here are five key things to look forward to. AlphaGo's historic victory against one of the best Go players of all time, Lee Sedol, was a landmark for the field of AI, and especially for the technique known as deep reinforcement learning. Reinforcement learning involves having a machine learn to solve a problem not through programming or explicit examples, but through experimentation combined with positive reinforcement.

Galileo viewed nature as a book written in the language of mathematics and decipherable through physics. His metaphor may have been a stretch for his milieu, but not for ours. Ours is a world of digits that must be read through computer science. It is a world in which artificial-intelligence (AI) applications perform many tasks better than we can. Like fish in water, digital technologies are our infosphere's true natives, while we analog organisms try to adapt to a new habitat, one that has come to include a mix of analog and digital components.

Machine learning is coming to the data center both to improve internal IT management and embed intelligence into key business processes. You have probably heard of a mystical deep learning, threatening to infuse everything from systems management to self-driving cars. Is this deep learning some really smart artificial intelligence that was just created and about to be unleashed on the world, or simply marketing hype aiming to re-launch complex machine learning algorithms in a better light? As DevOps is slowly taking over the IT landscape, its vital that IT pros understand it before jumping right into the movement. In this complimentary guide, discover an expert breakdown of how DevOps impacts day-to-day operations management in modern IT environments.