Look Up Trending Now Good Connection: Uplifting stories for a digital age Creator Playbook Say More Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Switch Off Mashable Voices Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List All Series'Silo' Season 3's twisty ending, explained Sam Haysom is the General Assignment Editor, UK, for Mashable. He covers entertainment and online culture, and writes horror fiction in his spare time. All products featured here are independently selected by our editors and writers. If you buy something through links on our site, Mashable may earn an affiliate commission. Last week, Season 3 delivered its most shocking episode so far .

Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Creator Playbook Mashable Voices Trending Now Say More Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List Switch Off In My Bag All Series'Silo' Season 3, episode 9's shocking ending, explained Well, that was a lot. Sam Haysom is the General Assignment Editor, UK, for Mashable. He covers entertainment and online culture, and writes horror fiction in his spare time. All products featured here are independently selected by our editors and writers. If you buy something through links on our site, Mashable may earn an affiliate commission.

Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Creator Playbook Mashable Voices Trending Now Say More Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List Switch Off In My Bag All Series'Silo' showrunner Graham Yost breaks down episode 9's bombshell ending and surprise musical guest Belen Edwards is an Entertainment Reporter at Mashable. She covers movies and TV with a focus on fantasy and science fiction, adaptations, animation, and more nerdy goodness. She is a member of the Critics Choice Association and the Television Critics Association, as well as a Tomatometer-approved critic. All products featured here are independently selected by our editors and writers. If you buy something through links on our site, Mashable may earn an affiliate commission.

Headlong's take on Karel Čapek's 1920 tale of romance and robots is rife with timely debates about tech's threat but at times the philosophical discussions drag on I f our world is currently thinking through the brave new future of generative AI and super intelligence, Karel Čapek's 1920 play RUR: Rossum's Universal Robots proves the notion of robot consciousness and rebellion is not a new anxiety. So does Mary Shelley's Frankenstein, which Čapek's drama resembles in its philosophical debates and moral warnings, despite its futurism. Ella Road adapts Čapek's play for our times in this Headlong and Schwarzman Centre co-production, its science apparently informed by research from Oxford University academics, which gives it a cutting-edge, real-world underpinning. The stage is presented as the operations office for the company, also named RUR, which is creating humanoids by mixing human flesh and blood with code and data at its headquarters on an island (a lovely, lush foliage and scaffold design by Loren Elstein). Dom (Trevor Fox) is the company's boss - a "dom" in more ways than one as he is having a Secretary-style, S&M romance with his robot personal assistant, Sulla (Tiffany Gray).

The New Movie Is Sparking a Right-Wing Backlash. Emily Wilson's 2017 translation of Homer's epic--the first by a woman--was called a woke "abomination" by online reactionaries. Christopher Nolan's film is facing similar critiques. Who'd have thought Helen of Troy would cause so much trouble? Earlier this year, certain quarters of the internet spun out at news that Kenyan-Mexican Oscar-winning actress Lupita Nyong'o was rumored to appear as the impossibly beautiful Spartan noble Helen--whose face, it was later written, launched a thousand ships--in Christopher Nolan's forthcoming Hollywood Homeric epic, The Odyssey The confirmation of her casting in May kicked off another wave of conniption fits.

The new sci-fi drama from Vince Gilligan posits an end-of-humanity scenario that everyone other than its protagonist can agree on. Even before her fellow-humans' contamination, Carol didn't seem to have much use for them. On the night that the world as we know it is destroyed, a novelist named Carol Sturka (played by Rhea Seehorn) sees cars and planes veer off course, an emergency room full of convulsing bodies, and her city, Albuquerque, on fire. The President dies under mysterious circumstances, and, more devastatingly for Carol, so does her live-in partner, Helen (Miriam Shor). Then, in less than an hour, the apocalypse cleans up after itself.

"I don't kill anyone who doesn't deserve it," says Sam (Ben Whishaw), the self-described "triggerman"--hit man--in the new Netflix spy thriller Black Doves. Sam, like the series' other main character, Helen (not her real name, played by Keira Knightley), works for Black Doves' eponymous organization. They are spies, more or less, but spies for hire, and when you get right down to it, most of Sam's gigs seem to be carrying out hits for drug dealers. Sam isn't the only hit man featured in a sleek, starry TV thriller this winter. On Peacock, Eddie Redmayne plays Alex in a new adaptation of Frederick Forsyth's 1971 novel The Day of the Jackal.

This work proposes a variational inference (VI) framework for hyperspectral unmixing in the presence of endmember variability (HU-EV). An EV-accounted noisy linear mixture model (LMM) is considered, and the presence of outliers is also incorporated into the model. Following the marginalized maximum likelihood (MML) principle, a VI algorithmic structure is designed for probabilistic inference for HU-EV. Specifically, a patch-wise static endmember assumption is employed to exploit spatial smoothness and to try to overcome the ill-posed nature of the HU-EV problem. The design facilitates lightweight, continuous optimization-based updates under a variety of endmember priors. Some of the priors, such as the Beta prior, were previously used under computationally heavy, sampling-based probabilistic HU-EV methods. The effectiveness of the proposed framework is demonstrated through synthetic, semi-real, and real-data experiments.

Theory of Mind (ToM) refers to the ability to attribute mental states, such as beliefs, desires, intentions, and knowledge, to oneself and others, and to understand that these mental states can differ from one's own and from reality. We investigate ToM in environments with multiple, distinct, independent AI agents, each possessing unique internal states, information, and objectives. Inspired by human false-belief experiments, we present an AI ('focal AI') with a scenario where its clone undergoes a human-centric ToM assessment. We prompt the focal AI to assess whether its clone would benefit from additional instructions. Concurrently, we give its clones the ToM assessment, both with and without the instructions, thereby engaging the focal AI in higher-order counterfactual reasoning akin to human mentalizing--with respect to humans in one test and to other AI in another. We uncover a discrepancy: Contemporary AI demonstrates near-perfect accuracy on human-centric ToM assessments. Since information embedded in one AI is identically embedded in its clone, additional instructions are redundant. Yet, we observe AI crafting elaborate instructions for their clones, erroneously anticipating a need for assistance. An independent referee AI agrees with these unsupported expectations. Neither the focal AI nor the referee demonstrates ToM in our 'silico-centric' test.

Click-Through Rate (CTR) prediction holds paramount significance in online advertising and recommendation scenarios. Despite the proliferation of recent CTR prediction models, the improvements in performance have remained limited, as evidenced by open-source benchmark assessments. Current researchers tend to focus on developing new models for various datasets and settings, often neglecting a crucial question: What is the key challenge that truly makes CTR prediction so demanding? In this paper, we approach the problem of CTR prediction from an optimization perspective. We explore the typical data characteristics and optimization statistics of CTR prediction, revealing a strong positive correlation between the top hessian eigenvalue and feature frequency. This correlation implies that frequently occurring features tend to converge towards sharp local minima, ultimately leading to suboptimal performance. Motivated by the recent advancements in sharpness-aware minimization (SAM), which considers the geometric aspects of the loss landscape during optimization, we present a dedicated optimizer crafted for CTR prediction, named Helen. Helen incorporates frequency-wise Hessian eigenvalue regularization, achieved through adaptive perturbations based on normalized feature frequencies. Empirical results under the open-source benchmark framework underscore Helen's effectiveness. It successfully constrains the top eigenvalue of the Hessian matrix and demonstrates a clear advantage over widely used optimization algorithms when applied to seven popular models across three public benchmark datasets on BARS. Our code locates at github.com/NUS-HPC-AI-Lab/Helen.