HBO's The Last of Us showed viewers in season one that it would lean heavily on the source video games for major plot points and general direction of the season while expanding on the universe, and season two has followed that to the most extreme end possible. Episode two sees Tommy and Maria lead the town of Jackson Hole against a massive wave of Infected, the likes of which we haven't seen in the show (or video games) yet. This was a complete invention for the show, one that gives the episode Game of Thrones vibes, or calls to mind a battle like the siege of Helm's Deep in Lord of the Rings: The Two Towers. It's epic in scale, with the overmatched defenders showing their skill and bravery against overwhelming odds; there is loss and pain but the good guys eventually triumph. That mass-scale battle is paired with the most intimate and brutal violence we've seen in the entire series so far, as Joel's actions finally catch up with him.

By pretty much every objective measure, HBO's adaptation of the hit postapocalyptic video game The Last of Us has been a roaring success. Never before has a video game narrative been molded into Emmy nominations and such warm reception among respectable critics, industry darlings, and people who have no idea what the term "one-shotting" means. You'd think that the devotees who first fell in love with the game back when it was originally released in 2013 would be toasting the cultural ascendance of their favorite medium--and especially how the story's complicated morality has impacted those who've never picked up a controller. And yet, for as long as the show has been on television, its most dogmatic fans have been caught up in a controversy of much inferior consequence: Specifically, they're furious that Bella Ramsey doesn't look much like Ellie. On the most basic level, this observation is correct.

This paper introduces a novel concept of interval probability measures that enables the representation of imprecise probabilities, or uncertainty, in a natural and coherent manner. Within an algebra of sets, we introduce a notion of weak complementation denoted as $\psi$. The interval probability measure of an event $H$ is defined with respect to the set of indecisive eventualities $(\psi(H))^c$, which is included in the standard complement $H^c$. We characterize a broad class of interval probability measures and define their properties. Additionally, we establish an updating rule with respect to $H$, incorporating concepts of statistical independence and dependence. The interval distribution of a random variable is formulated, and a corresponding definition of stochastic dominance between two random variables is introduced. As a byproduct, a formal solution to the century-old Keynes-Ramsey controversy is presented.

We give novel Python and R interfaces for the (Java) Tetrad project for causal modeling, search, and estimation. The Tetrad project is a mainstay in the literature, having been under consistent development for over 30 years. Some of its algorithms are now classics, like PC and FCI; others are recent developments. It is increasingly the case, however, that researchers need to access the underlying Java code from Python or R. Existing methods for doing this are inadequate. We provide new, up-to-date methods using the JPype Python-Java interface and the Reticulate Python-R interface, directly solving these issues. With the addition of some simple tools and the provision of working examples for both Python and R, using JPype and Reticulate to interface Python and R with Tetrad is straightforward and intuitive.

The 2013 video game "The Last of Us" was a hit with critics and players thanks to a powerful narrative. Ten years later, that story is headed to television on HBO in what the industry hopes is a harbinger for artfully adapting video games to TV and film. "The Last of Us," created by video game developer Naughty Dog and published by Sony Entertainment, follows hardened survivor Joel and his young protege Ellie as they navigate a post-pandemic world fighting people and mutated creatures. The zombie thriller, which premieres on Sunday, stars "Game of Thrones" veterans Pedro Pascal and Bella Ramsey. The PlayStation game won numerous awards, including "Game of the Year" at the 17th Annual Design Innovate Communicate Entertain summit (DICE), which honors video game industry professionals.

It may not appear so but Vladimir Bondarenko, a Ukrainian blogger, has a lot in common with Keenan Ramsey, a "growth specialist" at Silicon Valley communications firm RingCentral. Bondarenko is a former aviation engineer with a square jaw, close-cut brown hair and grey eyes who apparently took to writing anti-government screeds after Ukraine's aviation industry "collapsed". Ramsey, with her straight, pearly teeth, brown hair that falls past her shoulders and a flawless complexion, studied at New York University and is a fan of Bill Gates's ex-wife Melinda. Keenan and Vladimir are both fakes: digital faces generated by artificial intelligence software. Facebook discovered -- and eliminated -- the Bondarenko account in February, tracing it to a Russian troll farm.

Two Stanford researchers have fallen down a LinkedIn rabbit hole, finding over 1,000 fake profiles using AI-generated faces at the bottom. Renée DiResta and Josh Goldstein from the Stanford Internet Observatory made the discovery after DiResta was messaged by a profile reported to belong to a "Keenan Ramsey". It looked like a normal software sales pitch at first glance, but upon further investigation, it became apparent that Ramsey was an entirely fictitious person. While the picture appeared to be a standard corporate headshot, it also included multiple red flags that point to it being an AI-generated face like those generated by websites like This Person Does Not Exist. DiResta was specifically tipped off by the alignment of Ramsey's eyes (the dead center of the photo), her earrings (she was only wearing one) and her hair, several bits of which blurred into the background.

For years, automakers have told a specific story about how self-driving cars would arrive in the world. They would be shared and electric, fleets of ride-hail vehicles shuttling passengers like fancy taxis. General Motors and Lyft signed an agreement to pull it off back in 2016; Ford promised its robotaxis would debut by last year; Daimler said it would work with Uber to deploy fleets of Mercedes-Benzes. The logic was financial: Autonomous vehicle technology would be so expensive to develop that carmakers wouldn't be able to offer it to most drivers at prices they could afford. This vision carried profound implications: If city dwellers could depend on fleets of shared robotaxis for long trips, they could abandon the personal car altogether.

Yes, the Porsche Taycan is flying out of dealerships. And yes, it charges rapidly. But you may not have heard much about the most vanguard feature on Stuttgart's newest electric vehicle. For $474 up front -- or $12 a month -- Porsche HQ will remotely switch on what it calls the intelligent range manager, an over-the-air software update that limits the maximum speed and tweaks the car's navigation system to stretch how far it will travel on a single charge. Taking a cue from Tesla, Porsche is finally treating its 5,000-pound computer like the SAS platform that it is.

We consider the problem of estimating a particular type of linear non-Gaussian model. Without resorting to the overcomplete Independent Component Analysis (ICA), we show that under some mild assumptions, the model is uniquely identified by a hybrid method. Our method leverages the advantages of constraint-based methods and independent noise-based methods to handle both confounded and unconfounded situations. The first step of our method uses the FCI procedure, which allows confounders and is able to produce asymptotically correct results. The results, unfortunately, usually determine very few unconfounded direct causal relations, because whenever it is possible to have a confounder, it will indicate it. The second step of our procedure finds the unconfounded causal edges between observed variables among only those adjacent pairs informed by the FCI results. By making use of the so-called Triad condition, the third step is able to find confounders and their causal relations with other variables. Afterward, we apply ICA on a notably smaller set of graphs to identify remaining causal relationships if needed. Extensive experiments on simulated data and real-world data validate the correctness and effectiveness of the proposed method.