In the 1990s and early 2000s, technologists made the world a grand promise: new communications technologies would strengthen democracy, undermine authoritarianism, and lead to a new era of human flourishing. But today, few people would agree that the internet has lived up to that lofty goal. Today, on social media platforms, content tends to be ranked by how much engagement it receives. Over the last two decades politics, the media, and culture have all been reshaped to meet a single, overriding incentive: posts that provoke an emotional response often rise to the top. Efforts to improve the health of online spaces have long focused on content moderation, the practice of detecting and removing bad content.

If you had asked director Jonathan Nolan what his favourite film of the year was in the late 00s, more often than not he would have given you the name of a video game instead. "Having grown up with the entire history of the medium – I started playing Pong with my brother Chris many, many years ago – that was when games started to take on this level of audacity in their storytelling, their tone, the things they were doing," he says. "That's what I felt with [2008's] Fallout 3: the audacity. Nolan, who has just finished directing the first series of Amazon Prime's Fallout TV show, is sitting next to Todd Howard, the video-game director who led development on Fallout 3 and 4, talking to me a few hours before the premiere of the first two episodes. It is evident within minutes that Nolan understands games almost as well as Todd does. He says he's drawn to games where your options are open, you decide who you want to be and your decisions have an effect on the world around you: in other words, a game like Todd Howard's. The two come across like old friends, easy in each other's company, and enthusiastic about each other's work. "I talked to a lot of people about doing a Fallout movie or TV show and I kept saying no to everybody," Howard says. "I loved the work that Jonah had done in movies and in TV, and in a couple interviews he did, he mentioned his love of games ... I said to somebody, he's perfect.

Beauty, poise, and classical pageantry might not be what first springs to mind when you think of AI. But contestants in the world's first AI beauty pageant will need all of these in spades if they are to claim their share of a 20,000 ( 16,000) prize pool. The Fanvue Miss AI pageant will see AI-generated ladies go head-to-head in front of a panel of judges, including two AI influencers. These synthetic competitors will be judged on beauty, social media clout and their creator's use of AI tools. Will Monanage, Fanvue Co-Founder, says he hopes that these events will'become the Oscars of the AI creator economy.'

Last month, a new dating app called Volar launched in New York City, with the promise "We go on blind dates. So you don't have to." To sign up, you enter your name and phone number, then submit yourself to a brief interview with a chatbot matchmaker. When I made an account, Volar's bot asked what line of work I was in. "I'm a book critic," I replied.

"Noise" is a fuzzy word--a noisy one, in the statistical sense. Its meanings run the gamut from the negative to the positive, from the overpowering to the mysterious, from anarchy to sublimity. The negative seems to lie at the root: etymologists trace the word to "nuisance" and "nausea." Noise is what drives us mad; it sends the Grinch over the edge at Christmastime. ("Oh, the Noise! Noise!") Noise is the sound of madness itself, the din within our minds. The demented narrator of Poe's "The Tell-Tale Heart" jabbers about noise while he hallucinates his victim's heartbeat: "I found that the noise was not within my ears. . . . The noise steadily increased. . . . Yet noise can be righteous and majestic. The Psalms are full of joyful noise, noise unto the Lord. In the Book of Ezekiel, the voice of God is said to be "like a noise of many waters." In "Paradise Lost," Heaven makes "infernal noise" as it beats back the armies of Hell. At the same time, the word can summon all manner of ...

Millions of people have watched Mike Hughes die. It happened on February 22, 2020, not far from Highway 247 near the Mojave Desert city of Barstow, California. A homemade rocket ship with Hughes strapped in it took off from a launching pad mounted on a truck. A trail of steam billowed behind the rocket as it swerved and then shot upward, a detached parachute unfurling ominously in its wake. In a video recorded by the journalist Justin Chapman, Hughes disappears into the sky, a dark pinpoint in a vast, uncaring blueness.

This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs). These challenges are organized into three different categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges. Based on the identified challenges, we pose $200+$ concrete research questions.

This study introduces HQ-Edit, a high-quality instruction-based image editing dataset with around 200,000 edits. Unlike prior approaches relying on attribute guidance or human feedback on building datasets, we devise a scalable data collection pipeline leveraging advanced foundation models, namely GPT-4V and DALL-E 3. To ensure its high quality, diverse examples are first collected online, expanded, and then used to create high-quality diptychs featuring input and output images with detailed text prompts, followed by precise alignment ensured through post-processing. In addition, we propose two evaluation metrics, Alignment and Coherence, to quantitatively assess the quality of image edit pairs using GPT-4V. HQ-Edits high-resolution images, rich in detail and accompanied by comprehensive editing prompts, substantially enhance the capabilities of existing image editing models. For example, an HQ-Edit finetuned InstructPix2Pix can attain state-of-the-art image editing performance, even surpassing those models fine-tuned with human-annotated data. The project page is https://thefllood.github.io/HQEdit_web.

Text-to-Image (T2I) Synthesis has made tremendous strides in enhancing synthesized image quality, but current datasets evaluate model performance only on descriptive, instruction-based prompts. Real-world news image captions take a more pragmatic approach, providing high-level situational and Named-Entity (NE) information and limited physical object descriptions, making them abstractive. To evaluate the ability of T2I models to capture intended subjects from news captions, we introduce the Abstractive News Captions with High-level cOntext Representation (ANCHOR) dataset, containing 70K+ samples sourced from 5 different news media organizations. With Large Language Models (LLM) achieving success in language and commonsense reasoning tasks, we explore the ability of different LLMs to identify and understand key subjects from abstractive captions. Our proposed method Subject-Aware Finetuning (SAFE), selects and enhances the representation of key subjects in synthesized images by leveraging LLM-generated subject weights. It also adapts to the domain distribution of news images and captions through custom Domain Fine-tuning, outperforming current T2I baselines on ANCHOR. By launching the ANCHOR dataset, we hope to motivate research in furthering the Natural Language Understanding (NLU) capabilities of T2I models.

Recent advances in natural language processing (NLP) may enable artificial intelligence (AI) models to generate writing that is identical to human written form in the future. This might have profound ethical, legal, and social repercussions. This study aims to address this problem by offering an accurate AI detector model that can differentiate between electronically produced text and human-written text. Our approach includes machine learning methods such as XGB Classifier, SVM, BERT architecture deep learning models. Furthermore, our results show that the BERT performs better than previous models in identifying information generated by AI from information provided by humans. Provide a comprehensive analysis of the current state of AI-generated text identification in our assessment of pertinent studies. Our testing yielded positive findings, showing that our strategy is successful, with the BERT emerging as the most probable answer. We analyze the research's societal implications, highlighting the possible advantages for various industries while addressing sustainability issues pertaining to morality and the environment. The XGB classifier and SVM give 0.84 and 0.81 accuracy in this article, respectively. The greatest accuracy in this research is provided by the BERT model, which provides 0.93% accuracy.