'It was about degrading someone completely': the story of Mr DeepFakes - the world's most notorious AI porn site The hobbyists who helped build this site created technology that has been used to humiliate countless women. Why didn't governments step in and stop them? For Patrizia Schlosser, it started with an apologetic call from a colleague. "I'm sorry but I found this. Are you aware of it?"

Ten years ago, Eliot Higgins could eat room service meals at a hotel without fear of being poisoned. He hadn't yet been declared a foreign agent by Russia; in fact, he wasn't even a blip on the radar of security agencies in that country or anywhere else. He was just a British guy with an unfulfilling admin job who'd been blogging under the pen name Brown Moses--after a Frank Zappa song--and was in the process of turning his blog into a full-fledged website. He was an open source intelligence analyst avant la lettre, poring over social media photos and videos and other online jetsam to investigate wartime atrocities in Libya and Syria. In its disorganized way, the internet supplied him with so much evidence that he was beating UN investigators to their conclusions.

Watermarking generative-AI systems, such as LLMs, has gained considerable interest, driven by their enhanced capabilities across a wide range of tasks. Although current approaches have demonstrated that small, context-dependent shifts in the word distributions can be used to apply and detect watermarks, there has been little work in analyzing the impact that these perturbations have on the quality of generated texts. Balancing high detectability with minimal performance degradation is crucial in terms of selecting the appropriate watermarking setting; therefore this paper proposes a simple analysis framework where comparative assessment, a flexible NLG evaluation framework, is used to assess the quality degradation caused by a particular watermark setting. We demonstrate that our framework provides easy visualization of the quality-detection trade-off of watermark settings, enabling a simple solution to find an LLM watermark operating point that provides a well-balanced performance. This approach is applied to two different summarization systems and a translation system, enabling cross-model analysis for a task, and cross-task analysis.

On Monday, as Americans considered the possibility of a Donald Trump indictment and a presidential perp walk, Eliot Higgins brought the hypothetical to life. Higgins, the founder of Bellingcat, an open-source investigations group, asked the latest version of the generative-AI art tool Midjourney to illustrate the spectacle of a Trump arrest. It pumped out vivid photos of a sea of police officers dragging the 45th president to the ground. He generated a series of images that became more and more absurd: Donald Trump Jr. and Melania Trump screaming at a throng of arresting officers; Trump weeping in the courtroom, pumping iron with his fellow prisoners, mopping a jailhouse latrine, and eventually breaking out of prison through a sewer on a rainy evening. The story, which Higgins tweeted over the course of two days, ends with Trump crying at a McDonald's in his orange jumpsuit. All of the tweets are compelling, but only the scene of Trump's arrest went mega viral, garnering 5.7 million views as of this morning.

The viral AI-generated images of Donald Trump's arrest you may be seeing on social media are definitely fake. But some of these photorealistic creations are pretty convincing. Others look more like stills from a video game or a lucid dream. A Twitter thread by Eliot Higgins, a founder of Bellingcat, that shows Trump getting swarmed by synthetic cops, running around on the lam, and picking out a prison jumpsuit was viewed over 3 million times on the social media platform. What does Higgins think viewers can do to tell the difference between fake, AI images, like the ones in his post, from real photographs that may come out of the former president's potential arrest?

When fed a sufficient amount of training data, artificial intelligence techniques can be used to generate new ideas in several different ways. Is that creativity?

Shares of Mobileye Global rose 27% out of the gate in their trading debut, in one of the highest-profile and largest initial public offerings of the year. Intel automated car-driving unit initially traded at $26.71, above its IPO price of $21 a share. That gives Mobileye a valuation of more than $21 billion. The stock opened on the Nasdaq stock market a little before midday Wednesday, trading under the symbol MBLY. More than 3.5 million shares changed hands in the opening trade.

Corp. Chief Executive Pat Gelsinger said that recently imposed U.S. restrictions on semiconductor-industry exports to China were inevitable as America seeks to maintain technological leadership in competition with China. Speaking at The Wall Street Journal's annual Tech Live conference, Mr. Gelsinger said the restrictions, which require chip companies to obtain a license to export certain advanced artificial-intelligence and supercomputing chips as well as equipment used in advanced manufacturing, are part of a necessary shift of chip supply chains. "I viewed this geopolitically as inevitable," Mr. Gelsinger said. "And that's why the rebalancing of supply chains is so critical." A weekly digest of tech reviews, headlines, columns and your questions answered by WSJ's Personal Tech gurus.

The narrowness of AI will someday be replaced by artificial general intelligence. But will it have the capability to rival human intelligence and creativity? Some of the world's most advanced artificial intelligence (AI) systems, at least the ones the public hear about, are famous for beating human players at chess or poker. Other algorithms are known for their ability to learn how to recognize cats or their inability to recognize people with darker skin. But are current AI systems anything more than toys?

Some of the world's most advanced artificial intelligence (AI) systems, at least the ones the public hear about, are famous for beating human players at chess or poker. Other algorithms are known for their ability to learn how to recognize cats or their inability to recognize people with darker skin. But are current AI systems anything more than toys? Sure, their ability to play games or identify animals is impressive, but does this help toward creating useful AI systems? To answer this, we need to take a step back and question what the goals of AI are.