Steven Jones' porn business, which once netted him half a million in revenue per month, fell apart in 2013 amid stiff competition from free streamers like Pornhub. But the self-described science-fiction nerd now says he's getting back into the adult content game, thanks to artificial intelligence (AI) which he's leveraging to help customers make bespoke pornography depicting their AI-generated'dream girls.' More chaste AI-generated models, created by a marketing team in Barcelona, Spain, are already minting between 11,000 and 3,200 a month in advertising deals. Welcome to the brave new world of post-human modelling, where'posing' joins the ranks of big box store retail and rocket manufacturing among the industries where managers profit off a labor force of unpaid machines and virtual employees. Right now, these AI-crafted models are already realistic enough to fool people who see models up close much more than the rest of us.

Rep. Jay Obernolte was selected to lead the House task force on AI. Fox News Digital speaks with the California Republican about his goals for the panel and his own thoughts about the rapidly advancing technology. AI fakery is quickly becoming one of the biggest problems confronting us online. Deceptive pictures, videos and audio are proliferating as a result of the rise and misuse of generative artificial intelligence tools. With AI deepfakes cropping up almost every day, depicting everyone from Taylor Swift to Donald Trump, it's getting harder to tell what's real from what's not.

Scientists keep taking their own lives, and no one knows why. That's the central mystery at the start of 3 Body Problem, the new Netflix series based on a trilogy of sci-fi novels by Chinese author Cixin Liu. But it soon unfolds into something far grander: There's a mysterious VR video game, flashbacks to revolutionary China, shady billionaires, and strange cults. Liu's novels are beloved in China and have a smaller but similarly dedicated following among English-language readers, but they are hard science fiction--heavy on concept, light on character. More than once in the series, someone resorts to wheeling out a chalkboard to make their point, and there are scenes in the books that seem impossible to film: multidimensional structures collapsing in on themselves, a computer made up of millions of soldiers, nano-wires cutting through steel, diamond, flesh.

Sign up to receive our weekly cultural-recommendations newsletter. Science fiction has historically been considered a niche genre, one in which far-flung scenarios play out on distant planets. Today, though, such plots are at the center of our media landscape. The hosts are joined by Joshua Rothman, an editor and writer at The New Yorker, who makes the case for science fiction as an extension of the realist novel, tracing the way films like "The Matrix" and "Contagion" have shed new light on modern life. The boundaries between science fiction and reality are increasingly blurred: tech founders like Elon Musk and Jeff Bezos have cited classic sci-fi texts as inspiration, and terms like "red-pilling" have found their way into our political vernacular.

Aravind Srinivas credits Google CEO Sundar Pichai for giving him the freedom to eat eggs. Srinivas remembers the moment seven years ago when an interview with Pichai popped up in his YouTube feed. His vegetarian upbringing in India had excluded eggs, as it had for many in the country, but now, in his early twenties, Srinivas wanted to start eating more protein. Here was Pichai, a hero to many aspiring entrepreneurs in India, casually describing his morning: waking up, reading newspapers, drinking tea--and eating an omelet. Srinivas shared the video with his mother.

The Honor Magic 6 Pro is a strange phone. It folds innovative new AI features, secure 3D face unlock, cutting-edge battery tech, and a powerful camera into an expensively sleek body. But the MagicOS software is buggy, the camera is inconsistent, and it's one of the most expensive Android phones on the market. While the Honor Magic 6 Pro has delighted and impressed me over the past couple of weeks, it has also frustrated and confused me. It can be oh-so-slick one minute and trip up the next.

For decades, Eliezer Yudkowsky has been trying to warn the world about the dangers of AI. And now people are finally listening to him. But is it too late?

We present source detection and catalogue construction pipelines to build the first catalogue of radio galaxies from the 270 $\rm deg^2$ pilot survey of the Evolutionary Map of the Universe (EMU-PS) conducted with the Australian Square Kilometre Array Pathfinder (ASKAP) telescope. The detection pipeline uses Gal-DINO computer-vision networks (Gupta et al., 2024) to predict the categories of radio morphology and bounding boxes for radio sources, as well as their potential infrared host positions. The Gal-DINO network is trained and evaluated on approximately 5,000 visually inspected radio galaxies and their infrared hosts, encompassing both compact and extended radio morphologies. We find that the Intersection over Union (IoU) for the predicted and ground truth bounding boxes is larger than 0.5 for 99% of the radio sources, and 98% of predicted host positions are within $3^{\prime \prime}$ of the ground truth infrared host in the evaluation set. The catalogue construction pipeline uses the predictions of the trained network on the radio and infrared image cutouts based on the catalogue of radio components identified using the Selavy source finder algorithm. Confidence scores of the predictions are then used to prioritize Selavy components with higher scores and incorporate them first into the catalogue. This results in identifications for a total of 211,625 radio sources, with 201,211 classified as compact and unresolved. The remaining 10,414 are categorized as extended radio morphologies, including 582 FR-I, 5,602 FR-II, 1,494 FR-x (uncertain whether FR-I or FR-II), 2,375 R (single-peak resolved) radio galaxies, and 361 with peculiar and other rare morphologies. We cross-match the radio sources in the catalogue with the infrared and optical catalogues, finding infrared cross-matches for 73% and photometric redshifts for 36% of the radio galaxies.

Despite the promise of RLHF in aligning LLMs with human preferences, it often leads to superficial alignment, prioritizing stylistic changes over improving downstream performance of LLMs. Underspecified preferences could obscure directions to align the models. Lacking exploration restricts identification of desirable outputs to improve the models. To overcome these challenges, we propose a novel framework: Reinforcement Learning from Reflective Feedback (RLRF), which leverages fine-grained feedback based on detailed criteria to improve the core capabilities of LLMs. RLRF employs a self-reflection mechanism to systematically explore and refine LLM responses, then fine-tuning the models via a RL algorithm along with promising responses. Our experiments across Just-Eval, Factuality, and Mathematical Reasoning demonstrate the efficacy and transformative potential of RLRF beyond superficial surface-level adjustment.

Research and development in Unmanned Aerial Vehicles (UAVs) or Unmanned Aircraft Systems (UAS) has witnessed unprecedented scientific and commercial interest and growth, particularly during the last two decades. Although military applications dominated the global market for years, interest in using UAVs in civil and public domains increases exponentially, worldwide, albeit challenges related to integrating unmanned aviation into the national airspace. Sample applications include, but are not limited to, surveillance [1], search and rescue [2], aerial photography [3], fire monitoring [4], agriculture [5], and aerial delivery [6]. The listed applications refer to solely passive tasks, that is, tasks in which no UAV interaction with the environment is needed. However, contact with the environment is required in industrial and maintenance applications like bridge inspection, water damn inspection, high-voltage transmission line inspection [7], assembly tasks [8] or construction [9].