That may sound a little less weird once you know that Amy is an artificially intelligent personal assistant and the brainchild of a startup called X.ai. Then she takes over the task of coordinating schedules and setting the date. On Thursday, X.ai announced a fresh round of funding led by Two Sigma Ventures, a VC fund that invests in data-focused startups. That funding will help X.ai -- which has been operating in private beta since 2014 -- roll out out its service to the public this summer and release an enterprise edition before the end of the year. But are we ready for robots to take over our calendars?

But what the heck are neural networks and what do they mean for the future of computing and design? This is a sticky issue, because there's no single definition neural network that's universally agreed upon. In essence, a neural network is a computer program that tries to simulate the way a human mind works--more specifically, by simulating neurons themselves. In your brain, there are hundreds of billions of tiny cells called neurons, each of which is connected to maybe tens of thousands of its brethren in complicated, ever-changing webs. This charming interactive story is a great primer on how they work, but put (very) simply, neurons are how we learn.

During a conversation I had with Peter Norvig, we discussed about the kind of projects that we do at Machinalis and how strange does it feels to say that "we are a Machine Learning company": In many projects, the amount of effort spent on R&D on Machine Learning is usually a small fraction of the total effort, or it's not even there because we plan it for a future phase after building the application first. "Machine Learning development is like the raisins in a raisin bread: 1. You need the bread first 2. It's just a few tiny raisins but without it you would just have plain bread." A large company, at a higher level, decides to move away from standard tools, given that "our business and our data are different/peculiar", incorporating Machine Learning or Data Science into their processes. In these situations they call us just to get some raisins.

For instance, what would be the best way to block off the output of a layer with some probability? Should I draw a scalar from rng.binomial and and multiply it by output of the layer?

Loyalty can be a tough metric to master. It can also make or break your business. Once you can accurately calculate customer acquisition costs and lifetime value in a reliable way, you're an investment away from achieving scale. In a modern marketing ecosystem with 1-1 engagement becoming the rule rather than exception, the old "buy ten get one free" punch cards of yore won't cut it. Grubhub CMO Barbara Martin Coppola explained earlier this week at VentureBeat's Mobile Summit that anticipating your customers' next steps by developing a real-time feedback loop was critical to sustaining growth in mobile.

It learns your preferences and recognizes your body language, and adapts its actions with your comfort in mind.

Six teams of self-driving semi trucks finished long-distance journeys across western Europe Thursday morning, The Guardian reports, marking a major milestone in autonomous vehicle technology. The trucks left from countries like Sweden and Germany and ended their trip in The Netherlands. Humans weren't totally taken out of the equation in achieving the feat. The trucks were essentially playing "follow the leader," with a group of driverless semis matching the speed and overall route set by a human driver in the vehicle up front. Still, this so-called "platooning" maneuver can result in benefits like less traffic (as the trucks can drive much closer to one another) and increased fuel efficiency, meaning a potentially greener shipping industry.

If its first quarter is anything to go by, 2016 may be shaping up historically as the 1491 of space discovery. The month preceding Valentine's Day alone provided what would once have been a year's worth of cosmic news. Blue Origin, the aerospace company owned by Amazon CEO Jeff Bezos, took one giant leap toward a new Age of Discovery by relaunching and landing a rocket that had already made a round-trip journey through the stratosphere – a revolutionary moment in private space exploration. A pair of researchers kicked off a frenzied planet hunt by demonstrating that a massive, heretofore undetected planet could be lurking on the outer edge of our solar system. Cosmologist Stephen Hawking suggested that unforeseen effects of rapid scientific progress might, paradoxically, cause the extinction of life on Earth in the next thousand years or so, adding, "By that time, we should have spread out into space, and to other stars, so a disaster on Earth would not mean the end of the human race." And scientists announced they'd detected gravitational waves, evidence of a billion-year-old collision between black holes, thus confirming the final and most obscure principle of Einstein's theory of relativity – and opening a window that may soon offer a glimpse of the universe's very creation.

Inc. (AMZN - Analyst Report) acquired California-based startup Orbeus last year to enhance its artificial intelligence (AI) capabilities. The financial terms of the deal haven't been disclosed yet. Orbeus has carved its niche in image processing by using AI. This technology allows other software developers to add facial or object recognition to their own products. The use of artificial intelligence for image recognition has been gaining popularity in the tech sector as an increasing number of people are finding uses for it.

Nvidia introduces the DGX-1, a box that houses eight Tesla P100 cards with 16 GB of memory each. Touted as'world's first deep learning supercomputer,' it's designed to provide the computing needs of virtual reality and artificial intelligence researchers. Nvidia takes the wraps off the DGX-1, touting it as the "world's first deep learning supercomputer" that can deliver the computing needs of virtual reality developers and artificial intelligence researchers. Considering how the hardware maker already developed specialized GPU units for deep learning software, it shouldn't come as much of a surprise that it's going further on that front. "The DGX-1 is easy to deploy and was created for one purpose: to unlock the powers of superhuman capabilities and apply them to problems that were once unsolvable," Jen-Hsun Huang, cofounder and CEO of Nvidia, says, noting that researchers will no longer have to rely solely on home-built computing solutions.