When you are working, you have browsed information that is not relevant to your work, haven't you? I feel awkward when my boss is creeping behind. Of course, I can switch the screen in a hurry, but such behavior is suspicious, and sometimes I don't notice him. So, in order to switch the screen without being suspected, I create a system that automatically recognizes that he is approaching to me and hides the screen. Specifically, Keras is used to implement neural network for learning his face, a web camera is used to recognize that he is approaching, and switching the screen.

Rest assured that despite the constant Matrix-like scenarios, we're actually big fans of AI technology. But we're also sci-fi geeks, so we have to get it out somewhere. Earlier this year, I wrote about the basics of how machine learning works, and how we've been using it to train computer programs to beat us at the Chinese strategy game, Go. You'd think that teaching a computer how to think strategically and crush their opponents beneath their cybernetic heel would be enough for researchers, but they've decided to raise the bar again. Now, they want to teach computers just what it is that humans fear.

Artificial neural networks provide us incredibly powerful tools in machine learning that are useful for a variety of tasks ranging from image classification to voice translation. So what is all the deep learning rage about? The media seems to be all over the newest neural network research of the DeepMind company that was recently acquired by Google. They used neural networks to create algorithms that are able to play Atari games, learn them like a human would, eventually achieving superhuman performance. Deep learning means that we use artificial neural network with multiple layers, making it even more powerful for more difficult tasks.

Cyber threats are an ever-present danger to global economies and are projected to surpass the trillion dollar mark in damages within the next year. As a result, the cybersecurity industry is investing heavily in machine learning in hopes of providing a more dynamic deterrent. ABI Research forecasts machine learning in cybersecurity will boost big data, intelligence, and analytics spending to $96 billion by 2021. "We are in the midst of an artificial intelligence security revolution," says Dimitrios Pavlakis, Industry Analyst at ABI Research. "This will drive machine learning solutions to soon emerge as the new norm beyond Security Information and Event Management, or SIEM, and ultimately displace a large portion of traditional AV, heuristics, and signature-based systems within the next five years."

While researchers have been working on AI for many decades, the technology is finally making the transition into the commercial world. Recent developments in AI are mainly driven by the convergence of powerful computing infrastructure, an explosion in data availability and the development of large-scale deep learning algorithms. The pace of innovation in AI is truly breathtaking. According to the McKinsey Global Institute, AI is catalyzing change at a ten times quicker rate than mechanization during the industrial revolution and at roughly 3,000 times the impact. It is therefore not unreasonable to hear industry experts call AI "the new electricity" (Andrew Ng) and machine learning "the most powerful advance in engineering since the Scientific Method" (Steve Jurvetson).

The information security industry's rush to adopt machine learning will help businesses burn US$96 billion on big data, intelligence, and analytics by 2021, says research house ABI . The report by lead number cruncher Dimitrios Pavlakis claims User and Entity Behavior Analytics (UEBA) and "deep learning algorithm designs" will be widely adopted by security companies as they collectively put big data to work detecting threats. The former machine learning technology, UEBA, is correlation on steroids, capable of detecting anomalies that can indicate if staff logins have been compromised and are being tested across the enterprise network. It can learn the activities and services most typical of a user to generate alerts when something anomalous occurs, like login attempts to odd network shares. Vendors are buying up across the space including Splunk's buy of Caspida, and Arksight selling Securonix.

TensorFlow, Google's open source deep learning framework, has announced a release candidate for a full-blown version 1.0. Version 1.0 not only brings improvements to the framework's gallery of machine learning functions, but also eases TensorFlow development to Python and Java users and improves debugging. A new compiler that optimizes TensorFlow computations opens the door to a new class of machine learning apps that can run on smartphone-grade hardware. Since Python's one of the biggest platforms for building and working with machine learning applications, it's only fitting that TensorFlow 1.0 focuses on improving Python interactions. The TensorFlow Python API has been upgraded so that the syntax and metaphors TensorFlow uses are a better match for Python's own, offering better consistency between the two.

My mother began to lose her hearing while I was away at college. I would return home to share what I'd learned, and she would lean in to hear. Soon it became difficult for her to hold a conversation if more than one person spoke at a time. Now, even with a hearing aid, she struggles to distinguish the sounds of each voice. When my family visits for dinner, she still pleads with us to speak in turn. My mother's hardship reflects a classic problem for hearing aid manufacturers.

The American elections have been a hot topic in the office as we contemplate expanding our presence to the US. Since its debut in March, we have been entertained by the senseless tweets of DeepDrumpf, a Twitter bot created by Bradley Hayes, a postdoc at MIT. DeepDrumpf was trained on a few hours worth of transcripts of victory speeches and debates from the president elect using deep learning techniques. The tweets were constructed character by character and inspired by recurrent neural network models that had been previously employed to mimic Shakespearean speech. Although not the most sophisticated use of deep learning that we've seen, we must hand it to him for originality and capturing the zeitgeist.

We hear and read in the popular media about Artificial Intelligence (AI) all the time. We have movies about them. We hear about Elon Musk and Stephen Hawking warning us about AI's apocalyptic consequences. We hear from the World Economics forum about AI's effect on taking away our jobs. We here about how disruptive AI will be for businesses.