It's time to get over your Comcast X1 envy, Dish Network subscribers. Now you can stop changing channels with your finger like a sucker and start using voice commands instead, as Dish recently announced that 30 Voice Remote accessory is now available. The new remote only works with Hopper 3 DVRs and 4K Joey mini set-top boxes. The handheld unit can be used to control navigation, search, and content selection, as well as basic channel changes and DVR recordings. And you don't need to bark bizarre, stilted commands into the thing; Dish says you can use natural language when you search with the remote.

Last year, mobile operators lost 38 billion ( 28bn) of their revenue to fraud, according to the Communications Fraud Control Association's 2015 survey. International crime rings are successfully and profitably using highly sophisticated techniques to bulldoze through phone companies' anti-fraud defences. However, emerging big data machine learning applications are beginning to turn the tide. Padraig Stapleton, vice president of engineering at Argyle Data provides insights on how mobile operators are deploying big data and AI to protect themselves and their consumers. Mobile operators face an increasingly complex battle against sophisticated global cybercriminals.

Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it. If you already have a basic understanding of linear algebra, calculus, probability and programming: I recommend starting with Stanford's CS231n. The course notes are comprehensive and well-written. The slides for each lesson are also available, and even though the accompanying videos were removed from the official site, re-uploads are quite easy to find online.

Google DeepMind announced a partnership with Moorfields Eye Hospital National Health Service Foundation Trust that aims to determine whether or not machine learning technology can be used to analyze eye scans for earlier detection and interventions for eye degeneration. Google bought DeepMind in 2014 in a move to expand its artificial intelligence presence. The collaboration is the result of efforts by Pearse Keane, a consultant ophthalmologist at Moorfields, who contact DeepMind regarding a partnership to help conquer diabetic retinopathy and age-related macular degeneration (AMD) which combined affect more than 625,000 people in the U.K. and over 100 million individuals across the globe. The goal of the project is to create a digital tool that can read eye-scans and quickly recognize abnormalities. Age-related macular degeneration (AMD) is the most common cause of blindness in the U.K. "Every single day -- in the U.K. alone -- nearly 200 people lose sight from the severe, blinding form of this condition and globally the number of people with AMD is set to rise to nearly 200 million by 2020," DeepMind officials said.

Today, the National Science Foundation (NSF) announced that it will invest more than 400 million over the next seven years to support fundamental wireless research and to develop platforms for advanced wireless research in support of the White House's Advanced Wireless Research Initiative. These investments will support the research community in experimenting with and testing novel technologies, applications and services capable of making wireless communication faster, smarter, more responsive and more robust. In the last decade, wireless usage across the U.S. has expanded dramatically, with nearly 350 million smartphones, connected tablets and wearable devices in use -- more than double the number from a decade ago -- carrying more than 100,000 times the traffic they supported in 2008. Experts anticipate as many as 200 billion connected devices globally by 2020. The need for ultra-high-speed, high-bandwidth and low-latency (rapid-response) wireless connectivity will only increase.

Charles Givre is an unapologetic data geek who is passionate about helping others learn about data science and become passionate about it themselves. He has worked at Booz Allen Hamilton for the last five years as a data scientist for various government clientsand done some really neat data science work along the way, which hopefully saves U.S. taxpayers some money. Most of his work has been in developing meaningful metrics to assess how well the workforce is performing. For the last two years, Charles has been part of the management team for one of the company's largest analytic contracts. His responsibility has been to increase the amount of data science on the contract, both in terms of tasks and people.

James Barrat is a writer, director and producer of documentary films. He is also author of the nonfiction book Our Final Invention: Artificial Intelligence and the End of the Human Era. Time Magazine named him one of the 5 smartest people who believe AI could bring on the apocalypse and his book was named on of the 8 definitive tech books of 2013 bu Huffington Post. Be sure to check us out on www.waitwhatif.com,

Are algorithms taking over our jobs? Yes, yes they are... and that's a good thing. An algorithm is a series of steps with rules that help us solve problems and accomplish goals. And when we structure these steps and rules the right way we can automate the algorithm to establish Artificial Intelligence (A.I.). And it is this A.I. that helps us do our analytical heavy lifting so we can focus our time on doing the things that we're good at… the things we were hired to do.

Researchers from MIT have created an algorithm that can understand human visual cues, and predict the next action. This algorithm will help AI have the ability to be able to understand and predict human interaction and predict what could happen next. Hopefully, this could help in different fields in the future, like home assistants or intelligent security systems which could call the police or an ambulance immediately in case the need arises. MIT's Computer Science and Artificial Intelligence Laboratory created this deep learning algorithm. The researchers inputted different videos with human interaction onto the program and tested whether the programed actually "learned" sufficiently to be able to predict the right outcomes.

The Opening Session - Wake-up Call for Patient-Centered Radiology The SIIM 2016 Opening General Session was presented by Rasu B. Shrestha, MD, MBA, chief innovation officer, University of Pittsburgh Medical Center and President, UPMC Enterprises. The talk took the top-down approach of putting medical imaging and radiology in the context of significant macro-level changes taking place in populations, technologies, and the health care system. These changes, that UPMC is capitalizing on, highlight growing opportunities for new care models, new technologies and patient-centered care. More than ever today, this health care transformation stresses the imperative to spur changes in imaging, both incremental as well as paradigm-changing, to move toward patient-centric radiology and value-based imaging. Having lived through a century of "analog" radiology, followed by several decades of "digital" radiology, the next phase that lies ahead of medical imaging will be the phase of interoperability, analytics, and population health; one where "Context is King."