It's no secret that I love technology. From the domination of mobile to the latest in recruitment tools and gamification, and how video and live streaming is having an impact on hiring and training--changes are afoot that many of us couldn't have imagined 15 or so years ago. The reason this "tech meets HR" marriage is so exciting is how quickly the technology evolution has disrupted HR and enhanced the way HR professionals get things done. Now there's another big disrupter on the horizon, one that you would be wise to keep your eyes on: Artificial intelligence. In layman's terms, artificial intelligence (or AI) is an area of computer science where computers are "developed" to behave much the way humans do.

Artificial Intelligence helps choose what books you buy, what movies you see, and even who you date. It's in your smart phone, your car, and it has the run of your house. It makes most of the trades on Wall Street, and controls our transportation, energy, and water infrastructure. Artificial Intelligence is for the 21st century what electricity was for the 20th and steam power for the 19th. The Hollywood cliché that artificial intelligence will take over the world could soon become scientific reality as AI matches then surpasses human intelligence.

When it comes to robots replacing humans, we might think we have the upper hand since we're the ones who build and program them but that's not neccesarily the case anymore. Google is taking a different approach to training its robots – it's letting them teach each other. Researchers at Google have released a report showing how they connected 14 robotic arms together and used convolutional neural networks to let them teach themselves how to pick things up. The approach mimics how young children learn between the ages of one and four years old, and is essentially helping the robots to develop reliable hand-eye coordination. Typically, a robot would be programmed to carry out specific tasks, but this method shows how they can learn through trial-and-error in combination with a neural network – the same way a child learns how to do something by watching other people.

Artificial intelligence (AI) has the power to eradicate poverty and disease or hasten the end of human civilisation as we know it, celebrated scientist Stephen Hawking told an audience in Cambridge on Wednesday evening. Speaking at the launch of the Leverhulme Centre for the Future of Intelligence (CFI), Hawking said the rise of AI would transform every aspect of our lives and amounted to a global event at a par with the industrial revolution. CFI brings together four of the world's leading universities – Cambridge, Oxford, Berkeley and Imperial College, London – to explore the implications of AI for human civilisation. Hawking said: "Success in creating AI could be the biggest event in the history of our civilisation. But it could also be the last – unless we learn how to avoid the risks. Alongside the benefits, AI will also bring dangers like powerful autonomous weapons or new ways for the few to oppress the many. "We cannot predict what we might achieve when our own minds are amplified by AI.

As we continue through the OODA loop, we arrive at the Orient step. For a quick review of the OODA loop (see "What the heck is an OODA Loop?") The previous step was "Observe" where the intelligent agent (human or otherwise) learns what the things are in the world around the system that can influence the choices for appropriate behavior. So, having completed the Observe step, our agent knows what it has around it and it can start to explore what the impact of those things will be. As an example, think of an artificial intelligence that is trying to increase the ad revenue on a web site.

In the foothills of the Appalachian Mountains in a corner of South Carolina sits a town that should be economically dead. For decades, Greenville was the heart of the state's textile industry--and its economic engine. First attracted by the area's fast-moving rivers as a way to power looms, textile manufacturers employed tens of thousands of people here. Beginning in the 1970s, however, facing competition from lower-cost manufacturing regions like Mexico and Southeast Asia, these companies began to struggle. Over the next decades, many factories closed.

"We used Machine Learning as a Service (MLaaS) software for a year, and loved it. I thought it was exactly what we needed until I realized that the models I was writing on the side were better than the ones we had in production," explains Johnny Hilgers, the VP of Business Analytics at Spring Venture Group, an inside sales and marketing company. "We used Machine Learning as a Service (MLaaS) software for a year, and loved it. I thought it was exactly what we needed until I realized that the models I was writing on the side were better than the ones we had in production." What Johnny reffered to as Machine Learning as a Service software professes to make machine learning so simple that even a ML novice can click and point their way to a predictive algorithm that will help their company make smarter, data-driven decisions after only a few weeks of training.

If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" Here is a reading roadmap of Deep Learning papers! You will find many papers that are quite new but really worth reading. After reading above papers, you will have a basic understanding of the Deep Learning history, the basic architectures of Deep Learning model(including CNN, RNN, LSTM) and how deep learning can be applied to image and speech recognition issues. The following papers will take you in-depth understanding of the Deep Learning method, Deep Learning in different areas of application and the frontiers.

Japanese team plans AI medical supercomputer to rival Watson Dr. Watson, your Japanese brother should be arriving soon. An artificial intelligence system that can accurately diagnose a patient and suggest the best treatment is being developed by Kyoto University and Fujitsu Ltd. The hope is that it will emulate IBM's Watson supercomputer, which is famed for AI use in medicine using a big data system, and is named after Thomas J. Watson, the founder of IBM, rather than Sherlock Holmes' sidekick. The new system will analyze the genetic codes of patients to make its assessments. To run different simulations on relationships between diseases and a number of genes with integrated various data, it will be fed databases of worldwide medical records as well as gene information.

Could this be any more disturbing? A dozen years after they went off the air, the cast of Friends has been virtually reunited for a project that turned one of them into a video chatbot. As spotted by Prosthetic Knowledge, researchers at the University of Leeds used machine learning to create automated video avatars that speak in the voices of their characters. The result is a system that uses the original performances recorded by the actors to generate brief new scenes, starting with Matt LeBlanc's immortal Joey Tribbiani. The system was demonstrated this weekend at a European Conference on Computer Vision workshop.