If news, polls and investment figures are any indication, Artificial Intelligence and Machine Learning will soon become an inherent part of everything we do in our daily lives. Backing up the argument are a slew of innovations and breakthroughs that have brought the power and efficiency of AI into various fields including medicine, shopping, finance, news, fighting crime and more. TNW Conference won best European Event 2016 for our festival vibe. See what's in store for 2017. But the explosion of AI has also highlighted the fact that while machines will plug some of the holes human-led efforts leave behind, they will bring disruptive changes and give rise to new problems that can challenge the economical, legal and ethical fabric of our societies.

Here's a quick story you've probably heard before, followed by one you probably haven't. In 1979 a young Steve Jobs paid a visit to Xerox PARC, the legendary R&D lab in Palo Alto, California, and witnessed a demonstration of something now called the graphical user interface. An engineer from PARC used a prototype mouse to navigate a computer screen studded with icons, drop-down menus, and "windows" that overlapped each other like sheets of paper on a desktop. It was unlike anything Jobs had seen before, and he was beside himself. "Within 10 minutes," he would later say, "it was so obvious that every computer would work this way someday." As legend has it, Jobs raced back to Apple and commanded a team to set about replicating and improving on what he had just seen at PARC. And with that, personal computing sprinted off in the direction it has been traveling for the past 40 years, from the first Macintosh all the way up to the iPhone.

To understand how advances in artificial intelligence are likely to change the workplace -- and the work of managers -- you need to know where AI delivers the most value. Major technology companies such as Apple, Google, and Amazon are prominently featuring artificial intelligence (AI) in their product launches and acquiring AI-based startups. The flurry of interest in AI is triggering a variety of reactions -- everything from excitement about how the capabilities will augment human labor to trepidation about how they will eliminate jobs. In our view, the best way to assess the impact of radical technological change is to ask a fundamental question: How does the technology reduce costs? Only then can we really figure out how things might change. To appreciate how useful this framing can be, let's review the rise of computer technology through the same lens.

Diagnosis is one of the most important parts of medicine since doctors can hardly do anything to cure a patient if they don't know what the illness is. When it comes to tumors, the process is lengthy and complicated, increasing the chances of death. With the help of artificial intelligence, doctors can now cut the time needed for diagnosing tissue by 90 percent with considerable accuracy. Normally, diagnosing something like a brain tumor takes about 30 to 40 minutes, during which, doctors would need to leave the operation room in order to put the samples through the rigorous process for analysis, Futurism reports. With the help of advanced AI, however, this time can be cut down to a measly 3 to 4 minutes.

The words evoke images of sterile science-fiction environments, sleek robots and future-gazing articles from the past 20 years promising that, this time, AI is really taking over. The change has been more subtle than that – not a sudden revolution where we're servile to robots, but an undeniable shift in how AI is increasingly embedded in our technology, altering our lives as a result. This month, alongside our other regular coverage, we'll be paying special attention to AI with a number of interviews, features and other stories about this most exciting of technological advancements. Below, three of our writers give their thoughts on why it's so damned important. Way back in the 1990s, I spent a great deal of time thinking about AI.

"It is mind-boggling the amount of data our media consumption generates across all our devices. Making sense of it all becomes very difficult with a human-sized brain--you get lost in the details" Nicolas Bidon is Xaxis' global president and he's talking about the future of programmatic at Mobile World Congress in Barcelona. MWC is an apt setting for discussions on The Future. Away from these gigabytes of invention, the calm of Xaxis' networking garden is cool relief. So, how has programmatic evolved and what's next for the industry?

By 2020, an average internet user will use 1.5GB of traffic a day, and daily video traffic will reach 1PB, Intel predicts. A huge amount of data will be generated by autonomous vehicles, mobile devices, and internet-of-things devices. Every day, more information is being collected and sent to faster servers in mega data centers, which analyze and make sense of it. That analysis has helped improved image and speech recognition and is making autonomous cars a reality. Emerging superfast data networks like 5G -- a melting pot of wireless technologies -- will dispatch even more gathered information, which could stress data centers.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Enjoy today's videos, and let us know if you have suggestions for next week. This is one of the best things I have ever seen. In this video we present a new safety module for robots to ensure safety for different tools in collaborative tasks. This module, filled with air pressure during the robot motion, covers mounted tools and carried workpieces.

I recently stumbled across an old Data Science Stack Exchange answer of mine on the topic of the "Best Python library for neural networks", and it struck me how much the Python deep learning ecosystem has evolved over the course of the past 2.5 years. The library I recommended in July 2014,, is no longer actively developed or maintained, but a whole host of deep learning libraries have sprung up to take its place. Each has its own strengths and weaknesses. We've used most of the technologies on this list in production or development at indico, but for the few that we haven't, I'll pull from the experiences of others to help give a clear, comprehensive picture of the Python deep learning ecosystem of 2017. Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.

From helping you take care of email to creating personalized online shopping experiences, AI promises to transform the way we live and work. But with all the hype out there, how do we know which benefits we'll actually see? What is the top benefit you predict emerging from AI, and do you think the overall benefits will live up to the hype? The greatest benefit of AI -- which is already emerging -- is the elimination of repetitive tasks. From chat bots that can free up human staffers' times to work on more complex issues, to scheduling AIs like x.ai that eliminate the need to schedule meetings, AI will ultimately help humans spend more time focusing on creative and high-mental-effort activities.