When Bernie Sanders took an early, exploratory trip to Iowa, a curious crowd of 150 or so turned out at a college-town bookstore, where they listened politely as he raged against the billionaires and oligarchs he said were destroying America. The angry aria from the wild-haired, slouch-shouldered senator from Vermont was delivered beneath a sign reading "Science Fiction and Fantasy," which seemed an apt, if unintended, metaphor. How many people would take seriously a 73-year-old Jewish grandfather and democratic socialist, vying for the presidential nomination of a party he never joined and trying to topple one of the country's most powerful political dynasties? Sanders' thick Brooklyn baritone became a clarion call for the angry and the economically aggrieved, his rallies a sprawling coast-to-coast spectacle that blended the hippie vibe of Woodstock with the militancy of Occupy Wall Street. Sen. Bernie Sanders signaled Thursday night that he was winding down his bid for the Democratic presidential nomination, but he did not explicitly quit the campaign or endorse rival Hillary Clinton.

Microsoft, the famously conservative company best-known for producing office software like Windows, has taken the unexpected step of entering the burgeoning industry of marijuana. The company, founded by Bill Gates, announced a partnership on Thursday with Kind Financial, a Los Angeles firm that sells computer systems to governments that track legalized marijuana sales. Microsoft, which is headquartered in Washington, a state where recreational cannabis is now legal, is licensing Kind to sell Microsoft cloud services to authorities to monitor sales – rather than becoming directly involved in the trade itself. Kind, founded in 2013, sells pot tracking software to growers, sellers and governments. But even the cannabis company recognizes the foundations of its business is controversial in some quarters.

A robotic employee will be deployed instead of human council workers to answer customer queries. IPsoft said Amelia, its technology platform, will be deployed to work for Enfield Council in North London. Capable of analyzing natural language, she understands context, applies logic, learns, resolves problems and even senses emotions. IPsoft said Amelia will be deployed to work for Enfield Council in North London. Capable of analyzing natural language, she understands context and even senses emotions.

This is seven tons of semi-autonomous machine with sensors and a gun. This week, war robots came to Paris. On display at the Eurosatory 2016 Land and Airland Defence and Security tradeshow, RoBattle is a modular machine from defense firm Israel Aerospace Industries (IAI). Seven tons itself, it can carry three tons of sensors, weapons, and other tools it may need for fighting. And much like the first tanks that were built for the difficult war-scarred muddy ground of the Western Front, the RoBattle can climb over difficult terrain.

David Chalmers, who coined the phrase "Hard Problem of consciousness," is arguably the leading modern advocate for the possibility that physical reality needs to be augmented by some kind of additional ingredient in order to explain consciousness--in particular, to account for the kinds of inner mental experience pinpointed by the Hard Problem. One of his favorite tools has been yet another thought experiment: the philosophical zombie. Unlike undead zombies, which seek out brains and generate movie franchises, philosophical zombies look and behave exactly like ordinary human beings. Indeed, they are perfectly physically identical to non?zombie people. The difference is that they are lacking in any inner mental experience. We can ask, and be puzzled about, what it is like to be a bat, or another person.

This is a real laser gun that's marketed to real militaries in our real reality. Drones are smaller, slower, cheaper, and lower-flying targets than most airplanes, which makes them a weird threat to modern militaries. Even when just scouting, and especially if outfitted with explosives, the unmanned aerial vehicles are dangerous enough to warrant their destruction, but cheap enough that it doesn't make sense to use a missile. Enter the laser cannons, like this one on display at the Eurosatory 2016 Land and Airland Defence and Security tradeshow. Made by Germany's Rheinmetall, the Oerlikon Skyshield High Energy Laser is part of a larger system of sensors and weapons.

How do scientists decide what research to do? One would like to think that they take a suitably scientific approach to this question by thinking about important problems that need to be solved, and asking which of these problems could be solved given the time and money available. But are research projects actually proposed and funded in this way, or are there other forces at work? Particle physicists and astronomers realized decades ago that they needed to take a coordinated approach to planning so that they had accelerators and telescopes to work on. This "big science" approach involved agreeing on the long-term scientific goals in a given field and then getting the relevant funding agencies in different countries on board. This approach has been remarkably successful, as demonstrated by the recent detections of the Higgs boson and gravitational waves.

In addition to conducting pure research in artificial intelligence and machine learning (ML), the group will also develop new tools and products that make use of the technology. The company noted that it already offers several services to consumers that are based on machine learning technology, such as Google Translate, Photo Search, and Smart Reply for Inbox. The new research center is part of a broader initiative at Google to advance the field of machine learning. Researchers working at the company's existing engineering offices in Zurich have already made major contributions to the field, such as developing the conversation engine that powers the Google Assistant in the Allo smart messaging app, and developing the engine that powers Knowledge Graph. The Zurich office is already the company's largest engineering office outside of the US.

NVIDIA's (NASDAQ:NVDA) stock price is up 113% over the past 12 months, the company's revenues were up 13% year-over-year in the first quarter of its fiscal 2017, and CEO Jen-Hsun Huang says the company is seeing growth across all of its platforms. So why is NVIDIA in such a good position, and how might it stay there? Let's take a look at three things the company is doing well to answer that. The most obvious advantage the company has right now is its graphics processing unit (GPU) business. NVIDIA currently holds 76% of the discrete desktop GPU market, and it's continually growing the business.

The New York City taxi dataset is one of the largest publicly available datasets, with information about 1.1 billion NYC taxi rides. This dataset has been explored and visualized in a number of blog posts, using a variety of techniques and technologies (e.g., PostgreSQL, Apache Elastic Search). A recent blog post showed how to build ML models over one years' worth of this dataset using MRS running in a 4-node Hadoop cluster. In a new blog post, Microsoft Data Scientist Dmitry Pechyoni shows us how to build a binary classification model that will predict if a passenger will pay a tip. Dmitry was able to use Microsoft R Server (MRS) to drive the entire process of building and evaluating machine learning models over hundreds of millions of examples using a single commodity machine.