This walkthrough uses HDInsight Spark to do data exploration and train binary classification and regression models using cross-validation and hyperparameter optimization on a sample of the NYC taxi trip and fare 2013 dataset. It walks you through the steps of the Data Science Process, end-to-end, using an HDInsight Spark cluster for processing and Azure blobs to store the data and the models. The process explores and visualizes data brought in from an Azure Storage Blob and then prepares the data to build predictive models. Python has been used to code the solution and to show the relevant plots. These models are build using the Spark MLlib toolkit to do binary classification and regression modeling tasks.

Machine learning is becoming a buzzword--arguably an overused one--among companies that deal with networking. Recent announcements have touted machine learning capabilities at Google, Hewlett Packard Enterprise (HPE), and Nokia, for instance. But machine learning isn't being applied to networking itself. The intersection of machine learning and networking is where David Meyer, chief scientist at Brocade, has been working. After serving a term as the first chairman of the OpenDaylight Project's Technical Steering Committee (TSC), Meyer shifted his work into the realm of artificial intelligence.

Humans use language to tap into the knowledge of others and learn skills faster. This helps us hone our intuition and go through our daily activities more efficiently. Inspired by this, Google Research, DeepMind (its UK artificial intelligence lab), and Google X have decided to allow their robots share their experiences. Sharing the learning process among multiple robots, the research team has considerably expedited general-purpose skill acquisition of robots. Using an artificial neural network, we can teach a robot to achieve a goal by analyzing the result of its previous experiences.

We have already started talking about robots and have even floated many pilot projects where robots are acting as assistants to help customers in trivial matters. Industry experts have already mulled loss of jobs in BPO sector because of introduction to chatbots. We have just scratched the tip of AI and as we make strides in technology and make advancements, it is likely that far-fetched prophecy comes true. The question here is what does the future hold for robot applications?

As 2016 passes, and we look forward to the year ahead, here is my fourth annual list of the ten best long-form stories about technology from last year. Andreessen Horowitz general partner, Chris Dixon, has written a number of compelling pieces on Medium this year, but my favorite is a great overview of "What's Next in Computing", in which he highlights how each product era has two phases, the gestation phase and the growth phase. He highlights how fast the growth is happening and why. As hardware becomes small, cheap, and ubiquitous, we all have access to sophisticated technology. He then offers predictions in a range of rising technologies such as artificial intelligence, the Internet of Things, wearables, virtual reality, augmented reality.

Google researchers got an eye-scanning algorithm to figure out on its own how to detect a common form of blindness, showing the potential for artificial intelligence to transform medicine remarkably soon. The algorithm can look at retinal images and detect diabetic retinopathy--which affects almost a third of diabetes patients--as well as a highly trained ophthalmologist can. It makes use of the same machine-learning technique that Google uses to label millions of Web images. Diabetic retinopathy is caused by damage to blood vessels in the eye and results in a gradual deterioration of vision. If caught early it can be treated, but a sufferer may experience no symptoms early on, making screening vital.

WebRTC is the future of browser-to-browser communications, and continues to make inroads into the traditional, difficult, plug-in web communications world. WebRTC Summit 2017 New York continues our tradition of delivering the latest and greatest presentations within the world of WebRTC. Topics include voice calling, video chat, P2P file sharing, and use cases that have already leveraged the power and convenience of WebRTC. All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020. This number will continue to grow at a rapid pace for the next several decades.

Artificial intelligence captured the world's attention last year when it defeated humanity's champion at the game of Go. It was a landmark event for AI, much like the moment 20 years earlier when IBM's Deep Blue defeated Garry Kasparov at chess in 1997. Starting next week an artificial intelligence system named Libratus, developed by a team at Carnegie Mellon University, will try to establish a new milestone: beating some of the best human players at Heads-Up No-Limit Texas Hold'em poker. While Libratus may one day be listed in the history books alongside Deep Blue and Alpha Go, it's actually attempting to solve a very different kind of problem. Go and chess are perfect-information games: each player knows exactly what moves have been made and what space is left on the board to consider.

Artificial Intelligence, as we see it, is a collection of multiple technologies that enable machines to sense, comprehend and act--and learn, either on their own or to augment human activities. Compelling data reveal a discouraging truth about growth today. There has been a marked decline in the ability of traditional levers of production--capital investment and labor--to propel economic growth. Yet, the numbers tell only part of the story. Artificial intelligence (AI) is a new factor of production and has the potential to introduce new sources of growth, changing how work is done and reinforcing the role of people to drive growth in business.

While robots and computers will probably never completely replace doctors and nurses, machine learning/deep learning and AI are transforming the healthcare industry, improving outcomes, and changing the way doctors think about providing care. Machine learning is improving diagnostics, predicting outcomes, and just beginning to scratch the surface of personalized care. Imagine walking in to see your doctor with an ache or pain. After listening to your symptoms, she inputs them into her computer, which pulls up the latest research she might need to know about how to diagnose and treat your problem. You have an MRI or an xray and a computer helps the radiologist detect any problems that could be too small for a human to see.