MrBeast: AI means it's'scary times' for YouTube creators The world's biggest YouTuber, MrBeast, says the rapid advance of generative artificial intelligence (AI) is scary for the millions of creators currently making content for a living. AI tools that can create fully-formed videos from simple text prompts by users have made rapid advances in recent years. On social media, MrBeast, real name Jimmy Donaldson, asked what would happen to people like him when AI videos are just as good as normal videos. Fears about the impact AI will have on the jobs market are widespread - but particularly acute in the creative industries. In the film and video game industries, there has been extensive industrial action over the use of AI.

The filmmaker could not get Tiggy the alien to cooperate. He just needed the glistening brown creature to turn its head. But Tiggy, who was sitting in the passenger's seat of a cop car, kept disobeying. At first Tiggy rotated his gaze only slightly. Then he looked to the wrong side of the camera. Then his skin turned splotchy, like an overripe fruit. The filmmaker was not on a movie set, or Mars. He was sitting at his home computer in Los Angeles using a piece of AI software called FLUX Kontext to generate and regenerate images of the alien, waiting for a workable one to appear. He'd used a different AI tool, Midjourney, to generate the very first image of Tiggy (prompt: "fat blob alien with a tiny mouth and tiny lips"); one called ElevenLabs to create the timbre of Tiggy's voice (the filmmaker's voice overlaid with a synthetic one, then pitch-shifted way up); and yet another called Runway to describe the precise shot he wanted in this scene ("close up on the little alien as they ride in the passenger seat, shallow depth of field").

Clash of the White House titans: Two of Trump's most powerful lieutenants go to WAR with each other - after vicious leak sent shockwaves The troubled background of delivery man stabbed by Mark Sanchez... as he launches million-dollar lawsuit and sparks civil war at Fox Ominous warning for humanity as birds suddenly adopt'unsettling' behavior The TRUTH to the doting mother who slaughtered her children and husband told by those she'd been quietly tormenting for years Brazilian fashion influencer Junior Dutra dies at age 31 after alleged'fox eyes' procedure complications I've seen AI try to ESCAPE labs. The apocalypse is already here... and our children will be the first victims Trump brands NFL's Bad Bunny Super Bowl halftime show selection'absolutely ridiculous' Investigators reveal there is'no evidence' of arson after horror blaze destroyed South Carolina judge's beachfront home Functioning alcoholics hide in plain sight... so are YOU one? It sounds like the start of a sci-fi film, but scientists have shown that AI can design brand-new infectious viruses the first time. Experts at Stanford University in California used'Evo' - an AI tool that creates genomes from scratch. Amazingly, the tool was able to create viruses that are able to infect and kill specific bacteria.

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Zelda has asked people to stop sending her AI videos of her father, who died in 2014 at the age of 63. Zelda has asked people to stop sending her AI videos of her father, who died in 2014 at the age of 63. Film-maker tells the public to stop sending her videos, saying: 'You're not making art, you're making disgusting, over-processed hotdogs out of the lives of human beings' Zelda Williams, the daughter of the late actor and comedian Robin Williams, has spoken out against AI-generated content featuring her father. "Please, just stop sending me AI videos of Dad," Zelda wrote in an Instagram story on Monday . "Stop believing I wanna see it or that I'll understand, I don't and I won't. If you're just trying to troll me, I've seen way worse, I'll restrict and move on. But please, if you've got any decency, just stop doing this to him and to me, to everyone even, full stop. It's dumb, it's a waste of time and energy, and believe me, it's NOT what he'd want. "To watch the legacies of real people be condensed down to'this vaguely looks and sounds like them so that's enough', just so other people can churn out horrible TikTok slop puppeteering them is maddening.

Bayesian inference for doubly-intractable probabilistic graphical models typically involves variations of the exchange algorithm or approximate Markov chain Monte Carlo (MCMC) samplers. However, existing methods for both classes of algorithms require either perfect samplers or sequential samplers for complex models, which are often either not available, or suffer from poor mixing, especially in high dimensions. We develop a method that does not require perfect or sequential sampling, and can be applied to both classes of methods: exact and approximate MCMC. The key to our approach is to utilize the tractable independence model underlying an intractable probabilistic graphical model for the purpose of constructing a finite sample unbiased Monte Carlo (and not MCMC) estimate of the Metropolis--Hastings ratio. This innovation turns out to be crucial for scalability in high dimensions. The method is demonstrated on the Ising model. Gradient-based alternatives to construct a proposal, such as Langevin and Hamiltonian Monte Carlo approaches, also arise as a natural corollary to our general procedure, and are demonstrated as well.

Large-scale text-to-image diffusion models have become the backbone of modern image editing, yet text prompts alone do not offer adequate control over the editing process. Two properties are especially desirable: disentanglement, where changing one attribute does not unintentionally alter others, and continuous control, where the strength of an edit can be smoothly adjusted. W e introduce a method for disentangled and continuous editing through token-level manipulation of text embeddings. The edits are applied by manipulating the embeddings along carefully chosen directions, which control the strength of the target attribute. T o identify such directions, we employ a Sparse Autoencoder (SAE), whose sparse latent space exposes semantically isolated dimensions. Our method operates directly on text em-beddings without modifying the diffusion process, making it model agnostic and broadly applicable to various image synthesis backbones. Experiments show that it enables intuitive and efficient manipulations with continuous control across diverse attributes and domains.

Concept erasure techniques have been widely deployed in T2I diffusion models to prevent inappropriate content generation for safety and copyright considerations. However, as models evolve to next-generation architectures like Flux, established erasure methods (\textit{e.g.}, ESD, UCE, AC) exhibit degraded effectiveness, raising questions about their true mechanisms. Through systematic analysis, we reveal that concept erasure creates only an illusion of ``amnesia": rather than genuine forgetting, these methods bias sampling trajectories away from target concepts, making the erasure fundamentally reversible. This insight motivates the need to distinguish superficial safety from genuine concept removal. In this work, we propose \textbf{RevAm} (\underline{Rev}oking \underline{Am}nesia), an RL-based trajectory optimization framework that resurrects erased concepts by dynamically steering the denoising process without modifying model weights. By adapting Group Relative Policy Optimization (GRPO) to diffusion models, RevAm explores diverse recovery trajectories through trajectory-level rewards, overcoming local optima that limit existing methods. Extensive experiments demonstrate that RevAm achieves superior concept resurrection fidelity while reducing computational time by 10$\times$, exposing critical vulnerabilities in current safety mechanisms and underscoring the need for more robust erasure techniques beyond trajectory manipulation.