Machine-learning technologies are proving their mettle in new applications seemingly every day. According to Fast Company, IBM's Watson will debut at the U.S. Open by using its own unique perspective on the action to enhance video quality for both the IBM-powered U.S. Open website and smartphone apps with the same streamed content. This will include detailed analytics from scoring "hot-spots" on the court to the distance each player has traveled during the match. In addition, these machine-learning technologies are being continually trained to understand and transcribe human commentary. IBM's Watson doesn't just cater to the analytical cravings of some 15 million online U.S. Open viewers, either; it also provides useful insight into the athletes themselves.

This is the last of three articles about performance measures and graphs for binary learning models in Azure ML. Binary learning models are models which just predict one of two outcomes: positive or negative. These models are very well suited to drive decisions, such as whether to administer a patient a certain drug or to include a lead in a targeted marketing campaign. This final article will cover the threshold setting, and how to find the optimal value for it. As you will learn, this requires a good understanding of error cost, that is, the cost of inaccurate predictions.

As parents or educators, we share a common concern: how to deal with the various speeds at which students, arbitrarily put in the same classroom, study. It is inevitable that some will be slowed down while others will be left behind. Confronted with this situation, my wife and I decided to homeschool our 4 boys. It is a very heavy investment in time and presents a significant opportunity cost that not all families can afford. Artificial intelligence and a number of technology companies promise to personalize education through Adaptive Learning.

Predictive analysis: Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future. I am pretty sure I know what you are thinking: the above sounds somewhat Orwellian. However, what those of us adhering to the principles of data analysis are really striving for is simple: information that will assist management in increasing employee satisfaction. Use Case: Based on reports from the nGAGEMENT contact center solution - or for that matter IBM/Watson for customer service - a contact center manager finds out when specific members of his team are performing at their best.

Contrary to fictional portrayals of humans being herded like animals by Artificially Intelligent (AI) machines, realistic concerns about artificial intelligence are far more benign. Still, they are very important, and conversations have to begin in order to look at perhaps setting some ground rules. That's why the big players are stepping up. Researchers and scientists from large tech companies Google, Amazon, Microsoft, IBM, and Facebook have been meeting and discussing the future implications of AI for humans. While no hard details on the group's policies, objectives, or even it's name have come out, insiders have stated the group's intentions: to ensure that A.I. research is focused on benefiting people, not hurting them.

Amazon's voice-controlled speaker Echo and its Alexa software have been so successful that even rivals such as Google and reportedly Apple are developing competing versions. But Babak Hodjat, the inventor of the technology behind Apple's voice recognition system Siri, doesn't believe in Echo and Alexa's hype. He thinks they still lack one major component that makes human-machine conversations more complete: visual interactions. "It has to be augmented with all sorts of other visual cues, interactions, and maybe show some icons to allow the user to be able to click," Hodjat told Business Insider. "A solely speech recognition based system with no other user interface or contextual elements is incomplete."

Currently, developers use one of two methods to create speech programs. One involves using a large collection of words and speech fragments spoken by a single person, which makes sounds and intonations hard to manipulate. The other forms words electronically, depending on how they're supposed to sound. That makes things easier to tweak, but the results sound much more robotic. In order to build a speech program that actually sounds human, the team fed the neural network raw audio waveforms recorded from real human speakers.

In my last post, I showed how to find similar Game of Thrones episodes based on the characters that appear in different episodes. This allowed us to find similar episodes on an episode by episode basis, but I was curious whether there were groups of similar episodes that we could identify. A clustering algorithm groups similar documents together, where similarity is based on calculating a'distance' between documents. Documents separated by a small distance would be in the same cluster, whereas if there's a large distance between episodes then they'd probably be in different clusters. The KMeans algorithm clusters data by trying to separate samples in n groups of equal variance, minimizing a criterion known as the inertia or within-cluster sum-of-squares.

Intel has planted some solid stakes in the ground for the future of deep learning over the last month with its acquisition of deep learning chip startup, Nervana Systems, and most recently, mobile and embedded machine learning company, Movidius. These new pieces will snap into Intel's still-forming puzzle for capturing the supposed billion-plus dollar market ahead for deep learning, which is complemented by its own Knights Mill effort and software optimization work on machine learning codes and tooling. At the same time, just down the coast, Nvidia is firming up the market for its own GPU training and inference chips as well as its own hardware outfitted with the latest Pascal GPUs and requisite deep learning libraries. While Intel's efforts have garnered significant headlines recently with that surprising pair of acquisitions, a move which is pushing Nvidia harder to demonstrate GPU acceleration (thus far the dominant compute engine for model training) for deep learning, they still have some work to do to capture mindshare for this emerging market. Further complicating this is the fact that the last two years have brought a number of newcomers to the field--deep learning chip upstarts touting the idea that general purpose architectures (including GPUs) cannot compare to a low precision, fixed point, specialized approach.

Technology is continuing to play an integral part in sports. In cricket too, there are many areas where technology can be used. Machine learning will play an important role in Sports Analytics. We believe that we can use Machine Learning to analyze historical cricket games, and use this to continuously improve the Duckworth Lewis (D/L) Method of computing target scores in rain-shortened matches. The Current D/L method is a statistical method invented by statisticians Frank Duckworth and Tony Lewis.