Lee Pace Has Big Hopes for's Fourth Season WIRED spoke to Lee Pace on the eve of the season finale of about clone consciousness, robot gods, and what's next for the newly renewed show. In the world of prestige sci-fi, reigns as the biggest sleeper hit. Mention the Apple TV+ adaptation of Isaac Asimov's classic series in a group of friends and you'll suddenly find everyone has been secretly watching it. Something of a flawed masterpiece, the show, which wraps its third season Friday, has been averaging about 1.5 million hours watched per week in the US over the last month, according to Luminate. Reasons for the show's popularity are many, but it's seemed to have gained traction as it's become more, well, relevant. The series, like Asimov's books, focuses on a group of economists using a predictive algorithm to guide the destiny of humanity through the collapse of a galactic empire.

There's a moment in the new hit Amazon Prime Video series Fallout where the sunny protagonist, having emerged from her underground commune into a postapocalyptic hellscape, tries to convince a bloodthirsty mutant to follow the Golden Rule, to do unto others as you would have them do unto you. I expected the mutant--or Ghoul, to be more precise--to shoot back some nihilistic platitude in return, maybe a slang-ified version of a Thomas Hobbes quote. Instead, we get a perfect line: "Yeah, well, the wasteland's got its own golden rule," he replies. "'Thou shalt get sidetracked by bullshit every goddamn time.'" That rejoinder distills what makes Fallout, both the video game series and its television adaptation, so great. After all, getting sidetracked by bullshit is what Fallout has always been about.

Automated plot generation is the challenge of generating a sequence of events that will be perceived by readers as the plot of a coherent story. Traditional symbolic planners plan a story from a goal state and guarantee logical causal plot coherence but rely on a library of hand-crafted actions with their preconditions and effects. This closed world setting limits the length and diversity of what symbolic planners can generate. On the other hand, pre-trained neural language models can generate stories with great diversity, while being generally incapable of ending a story in a specified manner and can have trouble maintaining coherence. In this paper, we present an approach to story plot generation that unifies causal planning with neural language models. We propose to use commonsense knowledge extracted from large language models to recursively expand a story plot in a backward chaining fashion. Specifically, our system infers the preconditions for events in the story and then events that will cause those conditions to become true. We performed automatic evaluation to measure narrative coherence as indicated by the ability to answer questions about whether different events in the story are causally related to other events. Results indicate that our proposed method produces more coherent plotlines than several strong baselines.

As Netflix's profits have begun to wane, some business analysts have argued that, when compared to rivals like HBO Max or Amazon Prime, Netflix has a "quantity over quality" problem with its content. Critics have joined this bandwagon, turning on the streaming service's wide array of original material. This trend manifested itself most recently in the wake of the release of the television series Resident Evil, loosely based on the Capcom survival-horror video game from the 1990s. A week after its July 14th release, the show has been snubbed by critics, earning a 51% on Rotten Tomatoes, as well as absolutely savaged by viewers who rated the show on that website, leaving a bloodbath of one-star reviews and an "Audience Score" of 26%. Given the history of the Resident Evil movie franchise--six schlocky Milla Jovovich vehicles that contained, in total, exactly one memorable scene; one forgettable 2021 prequel--this kind of critical drubbing might be the expected outcome.

It is a truth, universally accepted, that video games do not translate well to the big screen. From Assassin's Creed to the Super Mario Bros movie, the result is usually a compromised monstrosity, ignorant of the source material and quickly disowned by the studios, directors and actors responsible for it. There have been exceptions – Detective Pikachu was weird but fine and the Resident Evil films have their fans. But films based on games are usually a mess. Have licensing managers been looking at the wrong screen the whole time?

If you enjoy reading, but still haven't foundyour next book to cozy up with, your smartphone might be able to suggest one. Artificial intelligence (AI) is now able to rank literature to predict the next bestseller – a kind of recommendation system, not based on metadata, but on the patterns and themes found in books. Publishers around the globe are mining all kinds of data, including what's in the books themselves, in search of the magic formula for evaluating a book's market potential. With more informed marketing, publishers hope to better target their customers. So, how does AI determine what we want to read?

It's hard to find someone who isn't a fan of "Game of Thrones." The TV show, which returns Sunday, has reached peaks of popularity that few shows do, and draws in fans of all shapes and sizes -- even computers. Maluuba, a Canadian startup, posted a YouTube video on Friday showing its artificial-intelligence software reading the synopsis for the fifth season of "Game of Thrones'" and immediately knowing all of the show's plot lines. It's the equivalent to a human, let's call him "John" for this example, who knows nothing about the show, has never seen it, takes one look at a Wikipedia page and instantaneously knows everything that's happening. "Who stabbed Jon Snow?" the Maluuba engineer asks the AI software.

Role-playing games, and other types of contemporary video games, usually contain a main storyline consisting of several causally related quests. As players have different motivations, tastes and preferences, it can be beneficial to customize game plotlines. In this paper, we present an offline algorithm for adapting human-authored game plotlines for computer role-playing games to suit the unique needs of individual players, thereby customizing gaming experiences and enhancing re-playability. Our approach uses an plan refinement technique based on partial-order planning to (a) optimize the global structure of the plotline according to input from a player model, (b) maintain plotline coherence, and (c) facilitate authorial intent by preserving as much of the original plotline as possible. A theoretical analysis of the authorial leverage and a user study suggest the benefits of this approach.