Narratives are made by the artful omission of facts. Never was this maxim more evident than in a gullible feature story that landed on the front page of the New York Times last fall, about a young woman's last-ditch bid for life extension as she succumbed to the ravages of brain cancer. A sober look at the case would have revealed it to be but the latest botched mortuary procedure conducted by a gang of creepy scam artists. Instead, through the good graces of the Times, this grim tale was spun into an inspirational saga of one person's courageous quest for a second chance at life, aided by medical visionaries on the verge of miraculous technological breakthroughs. Kim Suozzi died at age twenty-three in January 2013. After her first diagnosis, twenty-one months earlier, Suozzi chose to become one of the youngest people ever[*] to undergo an expensive form of ritualistic corpse mutilation called cryonic preservation. In pop culture, cryonics is perhaps best known as the plot device that transports the schlubby pizza delivery guy in Matt Groening's animated series Futurama into the thirty-first century. The decades-old quack procedure, which involves freezing corpse parts for later resuscitation, was for a long time apocryphally associated with such wealthy eccentrics as Walt Disney. It then caused a scandal in 2002 when it was widely reported that the body of baseball great Ted Williams had gone into deep freeze against the wishes of some in his family. In recent years, cryonics has regained an entirely undue aura of respectability as the thought leaders of Silicon Valley have trained their enterprising, disruptive vision on the conquest of disease and death.[**] Suozzi, an agnostic libertarian and aspiring neuroscientist, began taking cryonics seriously after discovering the work of the futurologist Ray Kurzweil through a cognitive science class at Truman State University in Missouri. After surgery and other treatments failed to stop the growth of her brain tumor, Suozzi determined that upon death she--or rather, her head--would be frozen and stored for decades, centuries, or millennia in the hope that one day, diligent, wonder-working doctors would transplant her consciousness into a new, healthy body, or perhaps onto a high-capacity hard drive. As a tech-savvy millennial, Suozzi turned to the chat website Reddit for help in raising the 80,000 she needed to fulfill her last wish. That got her well on her way, with about 7,000 reportedly raised.

The Gundersons want a robot that can solve simple tasks, like asking the bartender for a beer and then bringing it back to the table. To do this, they think that robots must solve the kind of problem that humans do instinctively when dealing with the world.

Video Optimization With Machine Learning is now a reality and publishers are intelligently making the most out of their O&O digital assets. The digital video industry is undergoing a transformation and machine learning is advancing the video user experience. Mobile, combined with video, is truly the definitive on-demand platform making it the fastest growing sector in digital content distribution. Video machine learning is a new field. The ability to crowd source massive human interactions on video content has created a new data-set.

In this video from the HPC User Forum in Tucson, Gary Paek from Intel presents: Intel's Machine Learning Strategy. Earlier this week, Intel announced the inception of the Intel Data Analytics Acceleration Library (Intel DAAL) open source project. Intel DAAL helps to speed up big data analysis by providing highly optimized algorithmic building blocks for all stages of data analytics (preprocessing, transformation, analysis, modeling, validation, and decision making) in batch, online, and distributed processing modes of computation. The open source project is licensed under Apache License 2.0.

Artificial intelligence and concerns over the long term consequences has come up again in the news week in the form of a Scientific American blog musing over how artificial intelligence will evolve -- "Is AI Dangerous? It Depends…" There is a certain amount of hand wringing over AI and, to a lesser extent, its branches such as machine learning and natural language processing. It also drew attention to notables who have voiced concern over AI including Bill Gates, Stephen Hawking and Elon Musk. The concerns tend to emerge from worst case scenarios and assume that even though AI can be used for beneficial purposes, what if the technology is turned against us? But many people applying AI to make healthcare delivery more efficient and automated don't see it that way.

As artificial intelligence gains a foothold in the mainstream of entrepreneurship, executives at IBM's Watson division predict cognitive computing technology will transform a variety of fields. Watson offers a set of tools to developers seeking to create AI-infused products. Roughly 360 companies, most of them startups, use Watson APIs and about 100 of those companies have created products that are already on the market. "It's a rapidly growing ecosystem and it's a rich set of tools," says Watson Senior Vice President Michael Rhodin. Watson Chief Technical Officer Rob High says Watson's products that provide AI capabilities to businesses are not aimed at replacing the work of humans, but at enhancing and augmenting what humans can do.

The National Science Foundation (NSF) is an independent federal agency that supports fundamental research and education across all fields of science and engineering. In fiscal year (FY) 2016, its budget is 7.5 billion. NSF funds reach all 50 states through grants to nearly 2,000 colleges, universities and other institutions. Each year, NSF receives more than 48,000 competitive proposals for funding and makes about 12,000 new funding awards. NSF also awards about 626 million in professional and service contracts yearly.

First of all let me tell you what is Open CV and what are the things that we can do using OpenCV. OpenCV is a open source C library for digital image processing and computer vision, which can be used to create real time face recognisation and using it with embedded robotics and micro controllers for purpose like differentiating a specific color from an image having various colors. Solution to all this we will cover in this course. "Few years back, I started learning programming and spent couple of months just to learn the basics. Then, for again a couple of months I spent my time learning advance of Open CV. Being in the same field for almost one year, I decided to start my own project. But I keep on stuck at various steps of my project as many of concepts were not cleared. I was not able to develop a simple software from the knowledge I gained. I was depressed and thinking to leave the programming. Then one day, I decided to give it one more try. I wrote down all the parts of my programming knowledge where I had weak concepts. I started visiting forums and posting my questions to sharpen my skills and doubt clearance. And again tried to create that project with fewer difficulties. I repeated the same method again and dig a lot. Now I got success, I am a professional programmer in C and OpenCV and now working with two companies."

The premise is simple: Correspondents, editors and producers from our newsroom share the pieces that have kept them reading, using the #NPRreads hashtag. Each weekend, we highlight some of the best stories. The irony was irresistible: The same week NPR went to Greenland to look at high suicide rates, The New York Times Magazine went to Greenland's neighbor, Iceland -- but for a story on high rates of happiness and how that contentedness is partly powered by the country's vulcanic geology. Iceland came in second on a list of world's happiest countries, despite its arctic weather. It has no public plazas or pubs, but it does have public pools, heated geothermically to hot-tub temperatures.

In the last few years, deep neural networks have dominated pattern recognition. They blew the previous state of the art out of the water for many computer vision tasks. Voice recognition is also moving that way. But despite the results, we have to wonder… why do they work so well? In doing so, I hope to make accessible one promising answer as to why deep neural networks work. I think it's a very elegant perspective. A neural network with a hidden layer has universality: given enough hidden units, it can approximate any function. This is a frequently quoted – and even more frequently, misunderstood and applied – theorem.