Still, a survey by the Pew Research Center also found widespread anxiety about the general impact of technological change. Three-quarters of Americans say it is at least "somewhat realistic" that robots and computers will eventually perform most of the jobs currently done by people. Roughly the same proportion worry that such an outcome will have negative consequences, such as worsening inequality.

In 2010, producer Bud Yorkin and his wife, Cynthia, approached Century City-based Alcon Entertainment with an audacious proposal: to make a sequel to the landmark science fiction film "Blade Runner." There were abundant reasons to avoid the project. Ridley Scott's 1982 original, about a futuristic society where androids known as "replicants" are almost indistinguishable from humans, is revered by notoriously protective science fiction fans. Sequels rarely live up to expectations, especially when the originals are several decades old. Like the movie's futuristic cops tasked with snuffing out rogue robots, moviegoers know how to tell a fake from the genuine article.

Sonic the Hedgehog - the spiky blue hero who has spent his life fighting Doctor Robotnik - is making his way to the big screen. The computer game character will move from SEGA consoles to film with a mix of CGI and live action techniques, according to the Hollywood Reporter. But will the move from the gamer's chair to the cinema seat pay off for Sonic and friends? We take a look at some of the other gaming giants that have become blockbusters - and whether they have been a success. The famous fighting game made its way out of the arcades and into live action when one of the action genre's biggest stars took on the role.

File photo: Chinese movie patrons wait in front of Hollywood star Keanu Reeves poster in the movie Matrix Reloaded showing at Paradise Warner Bros Cinema City in Shanghai July 18, 2003. Some people fear we humans are nothing more than pickled brains floating in a glass bowl as we're fed a false version of reality through a bundle of wires. Now a team of scientists at Oxford University has demolished the theory that we are all living in a computer simulation that's been masterminded by alien overlords. Science fiction fans and modern philosophers have long debated whether the world is actually the same as we percieve it to be. Following the popularity of 90s classic The Matrix, many have questioned whether the philosophical "Brain in a Vat" scenario may actually be our reality.

Two new Pixel phones and a competitor to Amazon's Echo Dot are among the products expected to be unveiled today at an event held by Google. The Pixel 2 and Pixel 2 XL phones will be iterations of the first Pixel phones released a year ago, according to leaks, while the Google Home Mini smart speaker is expected to be similar to Amazon's shrunk-down Echo Dot smart speaker, squeezing the voice-control features into a smaller package but requiring a separate speaker for high-quality music playback. It appears the phones are part of the company doubling down its efforts to compete directly with Apple in smartphone hardware. Additionally, they will continue to provide Google the opportunity to demonstrate the extent of its software prowess when freed from the requirement to work closely with other Android device manufacturers. These are the Google Pixel 2 and Pixel 2 XL https://t.co/cXCs560jNH

Makes me feel sad for the rest. Actually, that's a movie ("The Spy that Loved Me") that Netflix recommends for me since I'm a James Bond junkie and Netflix knows that. In fact, Netflix knows a lot about me as it knows a lot about all of its viewers, which is one reason why Netflix is a Wall Street darling and has rewarded its stockholders very well over the past couple of years (see Figure 1). But Netflix isn't doing anything that other organizations cannot do. To replicate Netflix's business success starts with thinking differently about the role of data and analytics in powering the organization's business.

David Fletcher was born in England in 1952. On leaving school he studied production engineering for five years then jumped on an aircraft bound for New Zealand where he's lived ever since. He was employed as an illustrator and cartoonist by New Zealand's largest daily newspaper, the New Zealand Herald, for three years, but for the last thirty years he's been pretending to work from home as a comic strip artist. He draws two daily strips called The Politician and Crumb the Blackbird, also several weekly strips. His cartoons are syndicated to Europe, Britain, US, Africa, Australia and New Zealand.

We know what Star Wars fans will be wishing for this Christmas -- aside from Sphero's R2-D2 and BB-9E toys. Ubtech, the company behind the dancing Lynx robot, is releasing a Stormtrooper bot. Although it can't teach you yoga, you can use voice commands to instruct it to ward off intruders (hopefully not with an actual blaster). Its facial recognition tech also allows it to store up to three faces in its memory bank. That way, it can shout at any uninvited guests that keep barging into your room. And, its accompanying mobile app packs an augmented reality game that lets you fight off the Resistance and launch First Order attacks.

I'm working with some friends on an idea for email classification and we're wondering what would be the best way to approach the problem. Essentially we're looking to create an application/Outlook extension that would classify emails into various categories like "Important/Not Important" or "Project email, Contract talks, Trash", we're not totally sure on categories at the moment, if it could be user defined it would be more useful I guess. How could one approach such a problem, is text-mining the right approach or should be we looking into AI/Machine Learning techniques or a combination of the two? I read a bit about Bayesian Probabilities and how using previous results sets you get a matrix table of probabilities and that's used to determine where new data would be categories. Is this the best approach or are there alternatives we should be looking at?

What are the state-of-the-art object detection and/or recognition architectures/papers? I found "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" very interesting, and I want to use the same idea on a very specific image domain. Once the objects are reliably detected, i plan to apply some sort of "information bottleneck" auto-encoder-style architecture for the task of image compression/decompression. Maybe even add adversarial training to get "sharper" reconstructions. Do you know some recent literature on this topic?