With a monthly payment option, it's now easier to test out Amazon Prime Video without making a year-long commitment to Amazon Prime. Combine that with the service's strong content lineup and it seems like Netflix could have cause for concern. But there are still a few things working against Amazon from a product perspective that keep it from being as good as Netflix, and they might not be so easy to fix. It's an issue of complexity, born out of Prime Video's history as a value-add that Amazon now feels is important enough to stand alone as its own product. Netflix was built, first and foremost, as a streaming video subscription service.

As a follow-up to my previous post "Using Machine Learning to predict Customer Behaviour", I wanted to address a similar topic but from an e-commerce perspective. How to you predict the behaviour of your visitors in your online store? Let's look at how Machine Learning can help you address each of the challenges posed by those four branches. In order to keep this post short, I've decided to split it in 4 parts where I'll cover each of the 4 segments. Let's start with product analytics.

And I immediately think they will die irrelevant.

Omoju Miller, a self-described futurist (someone who studies the future's possibilities), enjoys picturing tomorrow. As a Nigerian woman who settled in the Bay Area, she's already torn down historical barriers to work as a software engineer in Silicon Valley, a white man's world. But in envisioning a new society, Miller isn't thinking only of contemporary struggles; she's pondering what humanity will need next. Take one of her projects: Hiphopathy, where she's using machine learning to parse rappers' metaphorical language, in the hopes of teaching a computer to think conceptually, developing, in the process, a form of artificial intelligence. Recently, NationSwell spoke with Miller about true visionaries that inspire her and the lessons we can all take away from their avant-garde thinking.

I was jolted awake by the sound of airplanes from the Fort Lauderdale Airport. The musty South Florida air hung thick; the sketchy airport Sheraton air conditioning system offered no relief. It was March 24, and I prepared to make a visit to Magic Leap. By that time, I'd signed an NDA so onerous that I can't tell you much about the mixed-reality technology, how it works, or when it might be available. This is the part I can tell you.

Over the course of five seasons, Game of Thrones has killed off over 61 characters, including fan favorites such as Ned Stark, Oberyn Martell and (supposedly) Jon Snow. Now, a computer science class at Germany's Technical University of Munich has created a website dubbed "A Song of Ice and Data" to determine the fate of the HBO drama's remaining key players in the upcoming sixth season. Using a series of machine learning algorithms, the students have figured out the likelihood of each character meeting their end in the next 10 episodes. According to the site, Tommen Baratheon has the worst odds of survival--with a 97 percent chance of death--while Sansa Stark is the most likely to make it to next year at 3 percent. The group also applied the formula to both the show's previous seasons and George R.R. Martin's A Song of Ice and Fire series, and found it accurately predicted 74 percent of deaths.

As part of the BBC's Intelligent Machines season, Google's Eric Schmidt has penned an exclusive article on how he sees artificial intelligence developing, why it is experiencing such a renaissance and where it will go next. Until recently, AI seemed firmly stuck in the realm of science fiction. The term "artificial intelligence" was coined 60 years ago - on August 31 1955, John McCarthy proposed a "summer research project" to work out how to create thinking machines. It's turned out to take a bit longer than one summer. We're now entering the seventh decade, and just starting to see real progress.

With the next series of Game of Thrones set to hit our screens this month anxious fans may be wondering which one of their favourite characters is next on the hit list. The first five of the series have not been an easy watch, with the writers killing off key characters just when their luck starts to change and as audiences warmed to them.

With the next series of Game of Thrones set to hit our screens this month anxious fans may be wondering which one of their favourite characters is next on the hit list. The first five of the series have not been an easy watch, with the writers killing off key characters just when their luck starts to change and as audiences warmed to them. But researchers have developed a computer algorithm aimed at predicting the next character to die in the hit series. Students in Germany have developed a GoT-related computer algorithm which uses available data from the internet to predict the next character to die. By trawling the internet for data and clues, a team of computer scientists have created a model which crunches the numbers to work out which characters are most likely to die in the upcoming sixth series.

April 24 can't come soon enough for Game of Thrones fans eagerly awaiting the premiere of the hit show's sixth season. Naturally, most of us have been speculating wildly about the fate of our favorite characters for the past year, but now there's a clever app to help you withthat. The project, A Song of Ice and Data, was developed by a group of students of a JavaScript course at the Technical University of Munich. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May. It looks at 24 features of each character, the list of which includes attributes like their age, the House they belong to, whether they're married and how popular they are based on how many wiki pages link to them.