Artificial intelligence (AI) is definitely on the rise as many innovators and entrepreneurs recently vowed to invest more time and money on AI-driven programs. In fact, Google and chip-maker Movidius are planning to make AI more accessible through their new innovations. On a recent overnight shift in the emergency room, a woman who was having vague abdominal pain and chest discomfort for several days was referred to me. When her symptoms began, after searching google, she came up with a diagnosis list that included everything from influenza, to Zika, to lupus. The rich and famous Elon Musk has launched a beta version of a gym for the training of AI systems.

With automation becoming increasingly commonplace, tech boom wunderkinds, and everyone else, have been the debating the future of artificial intelligence. On one side Facebook founder Mark Zuckerberg, argues that more intelligent services aid humanity. On the other side is the founder of Tesla Motors and SpaceX, Elon Musk, who has frequently warned of humanity's doom at the hands of our own creations. "You know all those stories where there's the guy with the pentagram and the holy water, and he's like, sure he can control the demon?" Musk said during a talk on artificial intelligence at MIT in 2014.

New'deep learning' technique enables robot mastery of skills via trial and error. UC Berkeley researchers have developed algorithms that enable robots to learn motor tasks through trial and error using a process that more closely approximates the way humans learn, marking a major milestone in the field of artificial intelligence. They demonstrated their technique, a type of reinforcement learning, by having a robot complete various tasks -- putting a clothes hanger on a rack, assembling a toy plane, screwing a cap on a water bottle, and more -- without pre-programmed details about its surroundings. "What we're reporting on here is a new approach to empowering a robot to learn," said Professor Pieter Abbeel of UC Berkeley's Department of Electrical Engineering and Computer Sciences. "The key is that when a robot is faced with something new, we won't have to reprogram it. The exact same software, which encodes how the robot can learn, was used to allow the robot to learn all the different tasks we gave it."

At the 2016 Milken Global Conference on Beverly Hills, CA, innovators from many industries offer their visions of the future from gene editing to ending global warming. BEVERLY HILLS -- What is the future of humankind? That lofty topic is the big theme here at the Milken 2016 Global Conference in Beverly Hills, where some 3,500 politicians, scientists, technologists, sports stars and actors are focused on how to make the world a better place. How do we get folks to talk to one another again in a world dominated by digital devices? Will gene editing make us healthier and how far away are we from a major scientific breakthrough?

The Verge reports "The benefits of machine learning continue to trickle down to smartphones and gadgets, and chipmaker Qualcomm wants to help speed up the process. The company is launching a new software development kit for its "machine intelligence platform" Zeroth. This SDK will make it easier for companies to run deep learning programs directly on devices like smartphones and drones -- if they're powered by one of Qualcomm's chips, of course."

Katrina Rippel is a careful speaker who follows all the rules. Hao Chen is a more freewheeling conversationalist. And I'm a nonstop troublemaker, constantly blurting out whatever notions pass through my head. On a recent morning, the three of us met in cyberspace to find out how well (or poorly) we could communicate in a mixture of German, Mandarin, and English. Each of us spoke only our native language.

In Mediterranean waters, off the coast of France, a diver recently visited the shipwreck La Lune -- a vesssel in King Louis XIV's fleet -- which lay untouched and unexplored on the ocean bottom since it sank in 1664. But the wreck's first nonaquatic visitor in centuries wasn't human -- it was a robot. Dubbed "OceanOne," the bright orange diving robot resembles a mecha-mermaid. It measures about 5 feet (1.5 meters) in length and has a partly human form: a torso, a head -- with stereoscopic vision -- and articulated arms. Its lower section holds its computer "brain," a power supply, and an array of eight multidirectional thrusters.

LOS ANGELES--Will.i.am sounds congested and subdued, his batteries running low after a high-octane appearance on The Ellen Show earlier that day. He's longing for 2025, when an artificial intelligence will be able to tell him to take it easy. "Will, you sound like you are stuffy," he says in the voice of this fictional AI. "What did you eat recently? Stay away from sweets because they are causing you more mucus. You should get some rest tonight. I'm going to cancel your appointments from 7 pm. I've already bought you some epsom salts from CVS. Go and pick them up."

A California court has dismissed part of a lawsuit brought by Twitter that challenges U.S. government restrictions on what it can say about surveillance requests on its users. Twitter sued the government in 2014, alleging that the restrictions, which are common to all Internet service providers, infringe its First Amendment right to free speech. Earlier this year, the Department of Justice asked the federal district court in Oakland, California, to toss out the lawsuit. It argued that the Foreign Intelligence Surveillance Court (FISC) is a more suitable venue to hear the dispute, and that part of Twitter's argument didn't stand because the company isn't disputing document classification decisions made by the government. On Monday, a judge agreed with the government's latter argument but denied its request to shift the case to FISC.

Google's TensorFlow has been publicly available since November, 2015, and there is no disputing that, in a few short months, it has made an impact on machine learning in general, and on deep learning specifically. There is evidence of widespread acceptance via blog posts, academic papers, and tutorials all over the web. It is, of course, difficult to estimate true adoption rates, but TensorFlow's Github repository has nearly twice the number of stars of both the next most-starred machine learning project, Scikit-learn, and closest deep learning project, Berkeley Vision and Learning Center's Caffe. While not concretely indicative of TensorFlow having become the leader in the space, it is fairly easy to surmise that, given its fairly recent release, there has been considerable interest in, and use of, Google's deep learning library. For the most part, TensorFlow is relatively straightforward to use, and neural network afficianados without experience using the library could look at a given network's code and get an intuititive sense of what is going on.