An illustration of a large envelope fills the screen--I've received a new e-mail, my first in weeks. I click on the middle of the envelope and a note opens in size thirty-six font. It's a top-secret assignment for me, a renegade ex-C.I.A. agent who can kick higher than anyone else in the agency. "Looks like this old dog is heading back to the pound," I growl, and close the e-mail by turning off my entire computer. I pull up a digitized photo on the screen.

Do you like action movies? In my case, when I watch intense action scenes, I can't stop moving my body. Let's think about the hero of an action movie. They are well trained and have very sensible personalities so they can handle every situation in a short time. You may have seen this scene once!

"Terminator: Dark Fate" also marks the return of writer/producer James Cameron -- who directed the first two movies in the franchise, but wasn't involved in the three sequels that followed. Although monstrous machines have figured in movie plots since Fritz Lang's "Metropolis" in 1927, Schwarzenegger's performance in "The Terminator" set the stage for worries about out-of-control intelligent machines. Billionaire techie Elon Musk is among the best-known doomsayers. "I keep sounding the alarm bell, but until people see robots going down the street, killing people, they don't know how to react because it seems so ethereal," Musk said in 2017. On the other side of the debate, Oren Etzioni, the CEO of Seattle's Allen Institute for Artificial Intelligence, or AI2, keeps telling people to calm down.

In 2004, when Metal Wolf Chaos was first released on the original Xbox, it didn't have a snowball's chance in hell of making it out of Japan. Any sane publisher would have taken one look at this mad B-movie robot game, in which the 47th President of the United States (Michael) dons a mech suit and blows up most of the continent to quash an insurrection led by his former vice-president (Richard), and correctly assessed that it was at best a nonsense curiosity. Now, though, its developer FromSoftware is famous thanks to Dark Souls and we have cheap digital downloads, so this mostly dreadful and yet somehow fascinating relic of a long-dead era of video games has made an unexpected reappearance. Given the merest visual upgrade, it is otherwise an unreconstructed mad robot romp from the mid-oos, with appalling voice acting and overblown, poorly translated cutscenes that truly must be seen to be appreciated in all their awfulness. Lines such as "The fight will continue … as long as the America in my heart is still alive!" are so common that after a while I forgot even to cringe at them.

In this paper, we generalize the basic notions and results of Dempster-Shafer theory from predicates to formal concepts. Results include the representation of conceptual belief functions as inner measures of suitable probability functions, and a Dempster-Shafer rule of combination on belief functions on formal concepts.

How do companies like Amazon and Netflix know precisely what you want? Whether it's that new set of speakers that you've been eyeballing, or the next Black Mirror episode -- their use of predictive algorithms has made the job of selling you stuff ridiculously efficient. But as much as we'd all like a juicy conspiracy theory, no, they don't employ psychics. They use something far more magical -- mathematics. Today, we'll look at an approach called collaborative filtering.

A video software firm has come up with a way to prevent people from sharing their account details for Netflix and other streaming services with friends and family members. UK-based Synamedia unveiled the artificial intelligence software at the CES 2019 technology trade show in Las Vegas, claiming it could save the streaming industry billions of dollars over the next few years. Casual password sharing is practised by more than a quarter of millennials, according to figures from market research company Magid. Separate figures from research firm Parks Associates predicts that by $9.9 billion (£7.7bn) of pay-TV revenues and $1.2 billion of revenue from subscription-based streaming services will be lost to credential sharing each year. The AI system developed by Synamedia uses machine learning to analyse account activity and recognise unusual patterns, such as account details being used in two locations within similar time periods. Netflix's recommendation algorithm is pretty sophisticated these days, to the point where it can probably determine not only what you want to watch next, but what you'll eat for breakfast 13 years on Wednesday and the thread count of your sheets. And yet, it still has a tendency to spit out some peculiar recommendations.

Data scientists at 20th Century Fox and Google Cloud have developed machine-learning software that can analyze movie trailers and predict how likely people are to see those movies in theaters. A recent preprint research paper breaks down how the program, named Merlin, can now recognize objects and patterns in a trailer to understand movie scenes. Merlin can scan trailers and spot objects like "man with beard," "gun," "car," and decide whether the movie is an action flick or a crime drama based on the context in which those objects appear. "A trailer with a long close-up shot of a character is more likely for a drama movie," the study's authors write, "whereas a trailer with quick but frequent shots is more likely for an action movie." Merlin can use its knowledge of common tropes in trailers to understand how sequences of actions in trailers play into our expectations for genre films.

We describe an intelligent context-aware conversational system that incorporates screen context information to service multimodal user requests. Screen content is used for disambiguation of utterances that refer to screen objects and for enabling the user to act upon screen objects using voice commands. We propose a deep learning architecture that jointly models the user utterance and the screen and incorporates detailed screen content features. Our model is trained to optimize end to end semantic accuracy across contextual and non-contextual functionality, therefore learns the desired behavior directly from the data. We show that this approach outperforms a rule-based alternative, and can be extended in a straightforward manner to new contextual use cases. We perform detailed evaluation of contextual and non-contextual use cases and show that our system displays accurate contextual behavior without degrading the performance of non-contextual user requests.

By all objective measures, The Terminator represents the most feared cautionary tale of modern Hollywood: a broken franchise. Thirty-three years after Arnold Schwarzenegger became an international star playing a killer robot sent from the future to kill the mother of the leader of a postapocalyptic rebellion, there have been four sequels (and one TV series), and the three films without the involvement of creator James Cameron have turned off fans and led the property to bounce from studio to studio and reboot to reboot. Terminator: Genisys, a 2015 installment made by financier David Ellison's Skydance Media (Ellison bought rights from his sister, Megan Ellison, who acquired them in a 2011 auction for $20 million), seemingly, uh, terminated the prospect of future films. But this is Hollywood 2017, and no major franchise is truly dead. Ellison, along with distributor Paramount (Fox has international rights), has persuaded Cameron, who on Sept. 25 began filming four Avatar sequels, to shepherd a new Terminator for the era of Amazon drones, Facebook news bots and artificial intelligence-fueled anxiety. Calling it "a return to form that I believe fans of the franchise have been wanting since Terminator 2: Judgment Day," Ellison, 34, has for the past year worked secretly with Cameron and Deadpool's Tim Miller, who will direct the untitled sequel for a July 26, 2019, release. They assembled a writers room with scribes David Goyer, Charles Eglee, Josh Friedman and Justin Rhodes as well as Ellison, a lifelong Terminator fan (Cameron himself shows up once a week), and have crafted what they want to be a trilogy with Schwarzenegger, 70, and original star Linda Hamilton, 62, passing the torch to a young female lead.