These streaming devices will make any TV smart for 50 or less. Streaming devices are all the rage these days, whether you're cutting the cord or just want to access services like Netflix, Amazon Prime, HBO Now, Sling TV, or more on your TV. There are too many options, ranging in price from 30 up to nearly 200. But while the expensive options are nice, there are more cheap streaming devices than ever before. Here are all of our favorite options under 50, so you can pick the one that's right for you.

According to a survey of 122 founders, executives and investors in health-tech companies released today by Silicon Valley Bank, big data and artificial intelligence will have the greatest impact on the industry in the year ahead. Healthcare delivery and healthcare IT also promise the most growth in 2017. "Big data has been integral to our work at Celmatix. It has empowered physicians to be able to counsel women about their chances of having a baby, based on their relevant personal metrics, and not just their age," said Dr. Piraye Yurttas Beim, CEO at Celmatix. "It's an exciting time to be in a field where the pace of innovation continues to increase as both physicians and patients realize the potential of big data and personalized medicine."

These two industries have collided to augment and even build worlds from scratch, telling comprehensive and compelling stories. Yet one of the most astounding and useful advances comes not from a new type of virtual reality but in how marketers soon will be able to analyze the most sophisticated tool around: the human mind. I'm speaking here of the principle known as "cognitive intelligence," or C.I., which offers a crystal-clear window into the behaviors, wants and needs of the people around us. Cognitive is an umbrella term encompassing numerous aspects of how machine thinking mimics how we as humans think. Terms like machine learning, artificial intelligence, deep learning, adaptive neural networks, etc. all touch on components of what is encompassed in the broader term "cognitive."

Since the inception of modern fighter planes, it has always been an unspoken assumption that at some point, machines would be flying the aircraft instead of human pilots. This assumption became even more solid once Artificial Intelligence (AI) technology started to pick up. Now, new test results via simulations have shown that AIs are superior to human pilots, particularly when using fighter planes. Engineers that graduated from the University of Cincinnati programmed an AI that was able to outmaneuver and outfly fighter pilots, Wired reports. The program is called "ALPHA," and through multiple simulations against former United States Air Force Colonel Gene Lee, the AI came out significantly ahead.

The Obama Administration receives thousands of emails, phone calls and letters every day. And, since August, American citizens have been able to contact the president through Facebook as well, thanks the the White House's Messenger bot. Now, the administration is going a step forward and releasing the bot's source code onto the internet for anyone to use. This is done "with the hope that other governments and developers can build similar services...with significantly less upfront investment," according to the White House Blog. Anyone who's familiar with the Drupal 8 coding language will be able to take the administration's boilerplate code and easily launch a bot of their own.

Some of the work in the thesis was previously presented in [Gal, 2015; Gal and Ghahramani, 2015a,b,c,d; Gal et al., 2016], but the thesis contains many new pieces of work as well. There are two factors at play when visualising uncertainty in dropout Bayesian neural networks: the dropout masks and the dropout probability of the first layer. Uncertainty depictions in my previous blog posts drew new dropout masks for each test point--which is equivalent to drawing a new prediction from the predictive distribution for each test point -2 \leq \x \leq 2 . More specifically, for each test point \x_i we drew a set of network parameters from the dropout approximate posterior \boh_{i} \sim q_\theta(\bo), and conditioned on these parameters we drew a prediction from the likelihood \y_i \sim p(\y \x_i, \boh_{i}) . Another important factor affecting visualisation is the dropout probability of the first layer.

Ben Dickson is a software engineer and the founder of TechTalks. Insider threats are the biggest cybersecurity threats to firms, organizations and government agencies. This is something you hear a lot at security conference keynotes and read about in data breach reports, white papers and surveys -- and these insider threats are becoming increasingly more difficult to detect and prevent, as well as more frequent. This seemingly unstoppable growth accentuates the problem and shortcomings of current solutions, and warrants the need for new defensive technologies to detect and stop the digital daggers aimed at our backs. Data science -- the application of mathematics, big data analytics and machine learning to extract knowledge and detect patterns -- is an emergent, advanced technology area that is proving its effectiveness in the realm of cybersecurity, including fighting insider threats.

A linear dynamical system (A,B,C,D) is equivalent to the system (TAT {-1}, TB, CT {-1}, D) for any invertible matrix T in terms of the behavior of the outputs. A little thought shows therefore that in its unrestricted parameterization the objective function cannot have a unique optimum. A common way of removing this redundancy is to impose a canonical form.

Technology is positioned to reshape the future of work. But without critical components--acceptance and adoption by people--it can never achieve its full impact. Why, when digital technology is so powerful, should organizations still prize the contributions of their people? Rick Lash is a senior client partner with the firm. While digital technology is enabling disruption--think Uber, Airbnb or Amazon--it's also facilitating an era of incredible opportunity.

With the advances of modern data storage technology, chips the size of your fingernail are capable of storing an entire library's worth of knowledge, so one thing you might think computers do better than people is remember things. But according to Google Inc.'s DeepMind team, the artificial intelligence research group that developed AlphaGo, that is not entirely true. In a new paper published in the journal Nature, DeepMind has outlined a process where it trained a neural network to have human-like memory, giving it not only the ability to store data, but also to recall that information and use it to solve novel problems. "Neural networks excel at pattern recognition and quick, reactive decision-making, but we are only just beginning to build neural networks that can think slowly – that is, deliberate or reason using knowledge," the DeepMind team wrote in a recent blog post. "For example, how could a neural network store memories for facts like the connections in a transport network and then logically reason about its pieces of knowledge to answer questions?" DeepMind calls its new method differentiable neural computers, and the team demonstrated its capabilities using the London Underground, one of the largest public transit systems in the world.