OpenAI's report detailed how the five groups used the company's tech in their attempted influence operations. Spamouflage, a previously known group originating in China, used OpenAI's tech to research activity on social media and write posts in Chinese, Korean, Japanese and English, the company said. An Iranian group known as the International Union of Virtual Media also used OpenAI's tech to create articles that it published on its site.

The new artificial intelligence features Google announced just weeks ago are finally breaking through to the mainstream--albeit not in the manner Google might prefer. As you may have gleaned from recent coverage and chatter (or even experienced yourself), the autogenerated A.I. Overviews now sitting atop so many Google search results are giving answers that … well, to call them incorrect is true but doesn't quite nail it. Try surreal and ridiculous and potentially dangerous instead. Since their rollout, A.I. Overviews have told users to smoke cigarettes while pregnant, add glue to their home-baked pizza, sprinkle used antifreeze on their lawns, and boil mint in order to cure their appendicitis. To address the erroneous answers to both straightforward and jokey queries, Google appears to be addressing each incident one by one and tweaking the relevant Overviews accordingly. Still, the broken top-of-Google answers may even be spilling over into the search engine's other features, like its automatic calculator: One U.S.–based user found, posting a screenshot to X, that Google's tech couldn't even scan that the unit cm stands for centimeter, reading the measure as a whole meter.

Netflix has announced plans to release an animated series based on Minecraft. The best-selling video game of all time celebrates its 15th birthday this year, and the reveal dropped during an anniversary celebration. A short teaser trailer shows a green creeper character from the game scuttle into the middle of a black screen before exploding in a pixelated grey cloud. Once the fog clears, the camera sweeps into an underground cavern filled with lava. The shot eventually closes in on a red Netflix "N" logo on top of a volcanic rock before cutting to the words "Netflix x Minecraft".

While Minecraft was officially released in 2011, it was first introduced to the public back in 2009. This year, Minecraft is celebrating its 15th anniversary, and as part of the festivities surrounding that milestone, Netflix and Mojang Studios have announced that they're making an animated series based on the iconic sandbox game. It's a computer graphic-animated show that's being developed by Canadian studio WildBrain, which was also behind the animated Netflix shows Carmen Sandiego and Sonic Prime. The companies said the series will feature an original story based on new characters that show "the world of Minecraft in a new light." We'll most likely find out what that means exactly before the series launches when they start releasing previews and trailers.

I'm a reporter on the WIRED Politics desk, and I'm taking over for Makena this week to talk about politicians rising from the dead in India and the rapper Eminem endorsing opposition parties in South Africa. These things haven't really happened, obviously, but deepfakes created by generative AI have made it seem like they have. Already, we're seeing how politicians, campaigns, and regular people are using generative AI in elections. And this is only the beginning. So today, WIRED is launching a project to track it, all over the world.

The United States Department of Justice is quietly prosecuting a novel Espionage Act case involving a drone, a Chinese national, and classified nuclear submarines. The case is such a rarity that it appears to be the first known prosecution under a World War II–era law that bans photographing vital military installations using aircraft, showing how new technologies are leading to fresh national security and First Amendment issues. "This is definitely not something that the law has addressed to any significant degree," Emily Berman, a law professor at the University of Houston who specializes in national security, tells WIRED. "There's definitely no reported cases." On January 5, 2024, Fengyun Shi flew to Virginia while on leave from his graduate studies at the University of Minnesota and rented a Tesla at the airport.

The instances listed must be found "in the wild." This means we're only going to include instances where generative AI is being used or found out in the world. This list is almost certainly an undercount. However many instances of generative AI journalists, researchers, experts, or anyone else manages to find, it's likely that there are more. There may be some instances where it will be hard to tell whether a piece of media has been manipulated the old-fashioned way--a cheapfake--or if it's an actual use of generative AI.

In March, we saw the launch of a "ChatGPT for music" called Suno, which uses generative AI to produce realistic songs on demand from short text prompts. A few weeks later, a similar competitor – Udio – arrived on the scene. I've been working with various creative computational tools for the past 15 years, both as a researcher and a producer, and the recent pace of change has floored me. As I've argued elsewhere, the view that AI systems will never make "real" music like humans do should be understood more as a claim about social context than technical capability. The argument "sure, it can make expressive, complex-structured, natural-sounding, virtuosic, original music which can stir human emotions, but AI can't make proper music" can easily begin to sound like something from a Monty Python sketch.

Addressing online disinformation requires analysing narratives across languages to help fact-checkers and journalists sift through large amounts of data. The ExU project focuses on developing AI-based models for multilingual disinformation analysis, addressing the tasks of rumour stance classification and claim retrieval. We describe the ExU project proposal and summarise the results of a user requirements survey regarding the design of tools to support fact-checking.

Recent strides in large language models (LLMs) have yielded remarkable performance, leveraging reinforcement learning from human feedback (RLHF) to significantly enhance generation and alignment capabilities. However, RLHF encounters numerous challenges, including the objective mismatch issue, leading to suboptimal performance in Natural Language Understanding (NLU) tasks. To address this limitation, we propose a novel Reinforcement Learning framework enhanced with Label-sensitive Reward (RLLR) to amplify the performance of LLMs in NLU tasks. By incorporating label-sensitive pairs into reinforcement learning, our method aims to adeptly capture nuanced label-sensitive semantic features during RL, thereby enhancing natural language understanding. Experiments conducted on five diverse foundation models across eight tasks showcase promising results. In comparison to Supervised Fine-tuning models (SFT), RLLR demonstrates an average performance improvement of 1.54%. Compared with RLHF models, the improvement averages at 0.69%. These results reveal the effectiveness of our method for LLMs in NLU tasks. Code and data available at: https://github.com/MagiaSN/ACL2024_RLLR.