This blog post is the result of my efforts to show to a coworker how to get the insights he needed by using the streaming capabilities and concise API of Apache Spark. In this blog post, you'll learn how to do some simple, yet very interesting analytics that will help you solve real problems by analyzing specific areas of a social network. Using a subset of a Twitter stream was the perfect choice to use in this demonstration, since it had everything we needed: an endless and continuous data source that was ready to be explored. Spark Streaming is very well explained here and in chapter 6 of the ebook "Getting Started with Apache Spark," so we are going to skip some of the details about the Streaming API and move on to setting up our app. Let's see how to prepare our app before doing anything else.

Phrases such as "machine learning" and "artificial intelligence" are thrown around so often by so many people, they risk becoming buzzwords along the lines of "Big Data." But unlike "Big Data," which was always a somewhat-nebulous term, "machine learning" is a definitive process that, when applied correctly, can result in some impressive feats. For instance, check out how Google used it to radically transform the sophistication of Google Translate, one of its core services. For those tech professionals who wish to break into machine learning and artificial intelligence, make no mistake about it: there's a lot of education and training involved. Google wants to make that journey a little easier, though, with a new three-hour course that offers a quick overview of deep-learning fundamentals.

A group of Chinese ophthalmologists and scientists developed a deep learning algorithm to identify congenital cataracts, a rare eye disease responsible for nearly 10 percent of all vision loss in children worldwide. The researchers suggest the algorithm would assist humans, instead of replacing them. "For doctors, technology is not sufficient to determine the best course of treatment with 100 percent certainty, and doctors should therefore make good use of the machine's suggestion to identify and prevent the potential misclassification and complement their own judgment," said study co-author Haotian Lin, a professor of ophthalmology at Sun Yat-sen University. "The results of our comparative analysis showed that both artificial intelligence and human intelligence have strengths and limitations." Using CUDA, TITAN X Pascal GPUs and the cuDNN-accelerated Caffe deep learning framework, the researchers algorithm was able to catch the disease with more than 90% accuracy.

Researchers at the Massachusetts Institute of Technology have developed wearable tech that could help avoid crossed wires and miscommunication. Using artificial intelligence to detect the "tone" of a conversation based on speech patterns, the researchers were able to use a wearable band to "classify the overall emotional nature of the subject's historic narration". More simply, the band could spot signs of sadness, anger, boredom and so on from a person's voice. The project, by Tuka AlHanai and Mohammad Mahdi Ghassemi at CSAIL, is one of many aimed at'social coaching' designed to help people with chronic social disorders become more comfortable with the complexities of communication. Day-to-day life is underpinned by social interactions that play a key role in our mental health and development.

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes.

The world's best artificial intelligence poker player seems to know exactly when to hold'em and when to fold'em. An artificial-intelligence program known as Libratus has beat the world's absolute best human poker players in a 20-day No-Limit Texas Hold'em tournament, defeating four opponents by about $1.77 million in poker chips, according to Pittsburgh's Rivers Casino, where the "Brains vs. Artificial Intelligence" poker tournament was held. At the end of each day, at least one of the human players was beating the AI program. But in the end, it was not enough. "We appreciate their hard work, but unfortunately, the computer won," said Craig Clark, general manager of Rivers Casino.

Russia is carrying out a sustained campaign of cyber attacks targeting democracy and critical infrastructure in the West, UK Defence Secretary Sir Michael Fallon has warned. Moscow was "weaponising misinformation" in a bid to expand its influence and destabilise Western governments and weaken Nato, he said. Vladimir Putin had chosen to become a "strategic competitor" of the West. Sir Michael said it was vital alliance members strengthened cyber defences. His speech, at the University of St Andrews, comes as Theresa May is to use an informal summit in Malta to press EU Nato members to boost defence spending.

With Sunday's big game around the corner, you may be wondering how to outsmart your co-workers to win the office pool. You could stick with the Las Vegas line, which has New England favored to win by three. You could go with the theory that says that Atlanta lost its chance when it opted for red uniforms, as teams wearing white, like New England on Sunday, have won 11 of the last 12 championships. Or you could bet on Swish Analytics' artificial intelligence predictions to give you an edge. If you hate Tom Brady, you'll like what it says for Sunday's game.

Convolutional Neural Networks (CNN), a technique within the broader Deep Learning field, have been a revolutionary force in Computer Vision applications, especially in the past half-decade or so. One main use-case is that of image classification, e.g. You don't have to limit yourself to a binary classifier of course; CNNs can easily scale to thousands of different classes, as seen in the well-known ImageNet dataset of 1000 classes, used to benchmark computer vision algorithm performance. In the past couple of years, these cutting edge techniques have started to become available to the broader software development community. Industrial strength packages such as Tensorflow have given us the same building blocks that Google uses to write deep learning applications for embedded/mobile devices to scalable clusters in the cloud -- Without having to handcode the GPU matrix operations, partial derivative gradients, and stochastic optimizers that make efficient applications possible.

We've talked before about the rapid advances being made in the area of speech recognition. It's only a matter of time before companies like Doppler Labs augment our hearing such that we're able to experience real-time language translation. You'll soon be able to call him on that but only if you feel with 100% certainty that the translation is accurate. Why wouldn't language translation be accurate you ask? The simple answer here is one word.