Report says pattern seen in films such as Ex Machina risks contributing to lack of women in tech. A relentless stream of movies, from Iron Man to Ex Machina, has helped entrench systemic gender inequality in the artificial intelligence industry by portraying AI researchers almost exclusively as men, a study has found. The overwhelming predominance of men as leading AI researchers in movies has shaped public perceptions of the industry, the authors say, and risks contributing to a dramatic lack of women in the tech workforce. Beyond the impact on gender balance, the study raises concerns about the knock-on effects of products that favour male users because they are developed by what the former Microsoft employee Margaret Mitchell called "a sea of dudes". "Given that male engineers have repeatedly been shown to engineer products that are most suitable for and adapted to male users, employing more women is essential for addressing the encoding of bias and pejorative stereotypes into AI technologies," the report's authors write.

New AI systems such as ChatGPT, the overhauled Microsoft Bing search engine, and the reportedly soon-to-arrive GPT-4 have utterly captured the public imagination. ChatGPT is the fastest-growing online application, ever, and it's no wonder why. Type in some text, and instead of getting back web links, you get well-formed, conversational responses on whatever topic you selected--an undeniably seductive vision. But the public, and the tech giants, aren't the only ones who have become enthralled with the Big Data–driven technology known as the large language model. Bad actors have taken note of the technology as well. At the extreme end, there's Andrew Torba, the CEO of the far-right social network Gab, who said recently that his company is actively developing AI tools to "uphold a Christian worldview" and fight "the censorship tools of the Regime."

Scientists at a South Australian university are using biology from insects to build robots with a brain – technology that could become a game changer for police, defence and national security. "I'm giving a robot a brain so it can understand its environment," said Flinders University associate professor for autonomous systems, Dr Russell Brinkworth. His biologically-inspired robots have the ability to not just take a picture of the world, but interpret the surrounding environment and adapt accordingly. "Our current robots work well in structured environments that don't change. That sounds complex – but they're all the same," Dr Brinkworth said.

With a selection of creatives invited from across disciplines, the matter at hand would dominate popular culture for the next two decades: How could franchises expand beyond just one or two mediums? How could they achieve what EA's head of intellectual property development, Danny Bilson, called a "deepening of the universe"? That challenge and the book it inspired, Jenkins' Convergence Culture from 2006, turned out to be prophetic. At a time when movie attendance was on the rise, video games were hours long, and the internet was connecting everyone, Jenkins argued that media industries were missing a trick, and competing when they should've been collaborating. In response, he pitched a move into "transmedia storytelling"--a concept akin to the media mix in Japan at the time, where Pokémon dominated everything from anime to key rings. This would allow each medium to do what it does best, he wrote, "so that a story might be introduced in a film, expanded through television, novels, and comics, and its world might be explored and experienced through game play."

Finding out Taylor Swift was her 11th cousin twice-removed wasn't even the most shocking discovery Cher Scarlett made while exploring her family history. "There's a lot of stuff in my family that's weird and strange that we wouldn't know without Ancestry," says Scarlett, a software engineer and writer based in Kirkland, Washington. "I didn't even know who my mum's paternal grandparents were." In February 2022, the facial recognition search engine PimEyes surfaced non-consensual explicit photos of her at age 19, reigniting decades-old trauma. She attempted to get the pictures removed from the platform, which uses images scraped from the internet to create biometric "faceprints" of individuals.

In the ad, a woman in a white lace dress makes suggestive faces at the camera, and then kneels. But if you saw the video in the wild, you might not know that it's a deepfake fabrication. It would just look like a video, like the opening shots of some cheesy, low-budget internet porn. In top right corner, as the video loops, there is a still image of the actress Emma Watson, taken when she was a teenager, from a promotional shoot for the Harry Potter movies. It's her face that has been pasted on to the porn performer's.

Meme-makers and misinformation peddlers are embracing artificial intelligence tools to create convincing fake videos on the cheap. It wouldn't be completely out of character for Joe Rogan, the comedian turned podcaster, to endorse a "libido-boosting" coffee brand for men. But when a video circulating on TikTok recently showed Mr. Rogan and his guest, Andrew Huberman, hawking the coffee, some eagle-eyed viewers were shocked -- including Dr. Huberman. "Yep that's fake," Dr. Huberman wrote on Twitter after seeing the ad, in which he appears to praise the coffee's testosterone-boosting potential, even though he never did. The ad was one of a growing number of fake videos on social media made with technology powered by artificial intelligence. Experts said Mr. Rogan's voice appeared to have been synthesized using A.I. tools that mimic celebrity voices.

Regardless of how you feel, it's pretty clear that what Joel did was selfish. He could say that it was to ensure Ellie has a future, no matter the cost. But it's undeniable that he had Sarah, his murdered daughter, in mind as he "rescued" Ellie. In episode 6, HBO laid the groundwork with his new confession (not featured in the game) to his brother Tommy: Joel struggles with the possibility of losing another young girl in his life. His biggest fear is being unable to "protect" her.

This article contains spoilers for The Last of Us Episode 9, "Look for the Light." The Last of Us has been roundly praised as the best video-game adaptation ever made, even the first to be truly great, and part of that greatness is that you don't need to have played the original games to appreciate it. Although the HBO series is replete with moments from the games repeated beat for beat and even shot for shot, it's not hobbled by mindless fidelity to its source, and it finds ways to exploit its new medium that would never work if you were sitting in front of the TV with a PlayStation controller instead of a remote control. But in the final episode of the first season, "Look for the Light," The Last of Us returns to its source material in a way that comes close to wrecking everything the show has accomplished. Joel and Ellie have finally reached the goal they've been headed toward the entire season: the Firefly encampment in Salt Lake City. Joel has spent the past 20 years grieving the murder of his teenage daughter, Sarah, at the beginning of the Cordyceps pandemic, and the more recent death of his partner, Tess, and an uneasy reunion with his brother has underlined the lesson that it's best for him not to care for anyone at all.

State-sponsored trolls are the main actors of influence campaigns on social media and automatic troll detection is important to combat misinformation at scale. Existing troll detection models are developed based on training data for known campaigns (e.g.\ the influence campaign by Russia's Internet Research Agency on the 2016 US Election), and they fall short when dealing with {\em novel} campaigns with new targets. We propose MetaTroll, a text-based troll detection model based on the meta-learning framework that enables high portability and parameter-efficient adaptation to new campaigns using only a handful of labelled samples for few-shot transfer. We introduce \textit{campaign-specific} transformer adapters to MetaTroll to ``memorise'' campaign-specific knowledge so as to tackle catastrophic forgetting, where a model ``forgets'' how to detect trolls from older campaigns due to continual adaptation. Our experiments demonstrate that MetaTroll substantially outperforms baselines and state-of-the-art few-shot text classification models. Lastly, we explore simple approaches to extend MetaTroll to multilingual and multimodal detection. Source code for MetaTroll is available at: https://github.com/ltian678/metatroll-code.git.