This isn't a far-fetched idea, and not just because robots have a long track-record of automating human labor. There are already algorithms that can write stories. Bots can easily be programmed to write other basic stories--things like box scores and real estate listings, even obituaries. In January, Wired had a news-writing bot produce a remembrance of Marvin Minsky, the artificial intelligence pioneer. The result was a little dry compared with the obituary for Minksy written by a human at The New York Times--but the machine version was decent.

At the upscale Stanford Shopping Center in Palo Alto, California, people are taking selfies with a roving robot that looks like a cross between Wall-E's girlfriend and R2D2. It's actually a K5 robot security guard--a 300-pound, sensor-filled droid made by a startup called Knightscope that patrols the area and detects suspicious behavior. K5 is part of a small but growing number of human-scale mobile robots that are finding employment outside the confines of industrial settings like factories. They're invading consumer spaces including retail stores, hotels, and sidewalks in a quest to deliver services alongside human staff members for a fraction of the price of employing people to do a variety of typically unexciting tasks. The machines come with navigation capabilities and safety features to allow them to perform simple jobs autonomously without putting people at risk.

On any given night, 7 billion people might look into the sky and see things from a slightly different perspective. It's one world map of the night sky that encapsulates the way we all see it--subjectively from around the globe--through software. Technically, the map doesn't have 7 billion views from across the globe. Instead, it features eight different climate regions, sort of like a weather map might. So it has the polar sky, the arid sky, and the tropical monsoon sky, interspersed across the continents.

This blog is about building a classifier on the Azure Machine Learning platform for qualitative activity recognition of weight lifting exercises. This classifier predicts if an exercise has been done correctly (class A). We will use data from accelerometers on the belt, forearm, arm, and dumbell of 6 participants. The goal of this experiment is to predict the manner in which people did the exercise. This is the "classe" variable in the data set.

Suicide and depression are strongly linked, with suicide being the second leading cause of death for young people ages 15 to 34. Suicide rates in the United States have remained largely unchanged for the last 50 years. One of the most difficult jobs for psychiatrists is to assess suicide risk in our patients. A particular challenge is incorporating all of the clinical data that we obtain, such as patient's history of depression, their use of drugs, and their past suicide attempts, into producing a suicide risk assessment. What if we had a tool that would help us to do that in a more quantitative way, helping us to more accurately identify high risk patients, and give them preventative care?

Researchers from MIT's Computer Science and Artificial Intelligence Laboratory, the Harvard-Smithsonian Center for Astrophysics, and the MIT Haystack Observatory have developed a new algorithm that could help astronomers produce the first image of a black hole. The algorithm would stitch together data collected from radio telescopes scattered around the globe, under the auspices of an international collaboration called the Event Horizon Telescope. The project seeks, essentially, to turn the entire planet into a large radio telescope dish. "Radio wavelengths come with a lot of advantages," says Katie Bouman, an MIT graduate student in electrical engineering and computer science, who led the development of the new algorithm. "Just like how radio frequencies will go through walls, they pierce through galactic dust. We would never be able to see into the center of our galaxy in visible wavelengths because there's too much stuff in between."

Artificial intelligence is getting smarter--and more pervasive--by the day: Facebook recently announced that it's developing a way for its bots to not only read your private messages, but understand their meaning; Slack is on its way to having its chat feature answer questions about your job. And with that growing intelligence, of course, comes the growing fear that the machines will eventually rise up and end us all. That's why Google has outlined a plan for a kill switch that can stop algorithms before they get out of control--or at least, too out of control. If there's one company that should be concerned about its creations outsmarting humanity, it's Google. Known AI-phobic Elon Musk recently implied Google is the one company whose machines he fears might get too smart. The company's robot made headlines recently when it handily beat the world's best Go player in a head-to-head series.

Stephen Hawking has warned that artificially intelligent machines could kill us because they are too clever. Such computers could become so competent that they kill us by accident, Hawking has warned in his first Ask Me Anything session on Reddit. A questioner noted that Professor Hawking's ideas about artificial intelligence are seen as "a belief in Terminator-style'Evil AI'", and asked how he would present his own beliefs. "The real risk with AI isn't malice but competence," Professor Hawking said. "A super intelligent AI will be extremely good at accomplishing its goals, and if those goals aren't aligned with ours, we're in trouble. "You're probably not an evil ant-hater who steps on ants out of malice, but if you're in charge of a hydroelectric green energy project and there's an anthill in the region to be flooded, too bad for the ants.

On the heels of such great discoveries as the germ theory, X-rays, DNA, and Penicillin, the next stage of our growth in healthcare will come from Artificial Intelligence (A.I.). This includes effective machine learning, neural networks, and a promise of greater pattern recognition, data analysis, 'general thinking', decision-making and efficiency. Up until recently, a large percentage of our data was not digitized, nor did we have the storage and processing power. Now that we have this capability, we will soon see significant steps forward in A.I. innovation, growth of neural networking, and deep-learning technology including: Take a moment and see what's on the horizon. Venture funding will continue to increase in the digital health sector, with respect to dollars invested and deals made.

Look! Up in the sky! Is it a bird or is it a plane? If you're in Nevada, it could be the world's first passenger drone. The electric Ehang 184-passenger drone has been given clearance for testing in the airspace above the Silver State, the Guardian reported Wednesday. The drone can carry people inflight for 23 minutes.