Brands said it is bringing an "AI-first" mentality to fast food. FAST-FOOD INNOVATIONS: Yum! Brands, the operator of KFC, Pizza Hut, Taco Bell and The Habit Burger Grill restaurants, is embracing technology with plans for "AI-powered" fast food, according to a Wall Street Journal report. Companies will be making the move toward AI to power their restaurants. CREATIVE INFRINGEMENT: Katy Perry and Miranda Lambert are just some of the more than over 200 names who have signed a letter speaking out for musicians' rights as artificial intelligence continues to expand its reach. POWER SURGE: Global energy demand is projected to surge in coming years amid the growth of artificial intelligence, which requires massive amounts of electricity.

Billie Eilish, Nicki Minaj and Katy Perry are among 200 high-profile artists calling for the'predatory' use of AI in the music industry to be stopped. In an open letter, several of the world's biggest stars have warned the tech'will set in motion a race to the bottom' if left unchecked. The use of AI to steal artists' voices, likeness, and sound is an'assault on human creativity', they said, and would'destroy the music ecosystem'. Issued by the Artists Rights Alliance (ARA), the letter calls for a ban on AI tools that undermine or replace human songwriters or their work. The move is part of an industry-wide push for better regulation of generative AI, the technology behind chatbots like ChatGPT and image generators like Midjourney.

Fox News Flash top entertainment and celebrity headlines are here. George Carlin's estate has agreed to a settlement with the media company it sued earlier this year over the use of artificial intelligence. In January, Carlin's estate sued the podcast company, Dudesy, for recreating Carlin's iconic comedic style in an hour-long special titled "George Carlin: I'm Glad I'm Dead." The settlement indicates that Dudesy is required to permanently remove the special and cannot use Carlin's image voice or likeness in the future without written consent from the estate. According to The Associated Press, the settlement agreement was approved by both sides and awaits a judge's approval.

Large language models (LLMs) often generate content that contains factual errors when responding to fact-seeking prompts on open-ended topics. To benchmark a model's long-form factuality in open domains, we first use GPT-4 to generate LongFact, a prompt set comprising thousands of questions spanning 38 topics. We then propose that LLM agents can be used as automated evaluators for long-form factuality through a method which we call Search-Augmented Factuality Evaluator (SAFE). SAFE utilizes an LLM to break down a long-form response into a set of individual facts and to evaluate the accuracy of each fact using a multi-step reasoning process comprising sending search queries to Google Search and determining whether a fact is supported by the search results. Furthermore, we propose extending F1 score as an aggregated metric for long-form factuality. To do so, we balance the percentage of supported facts in a response (precision) with the percentage of provided facts relative to a hyperparameter representing a user's preferred response length (recall). Empirically, we demonstrate that LLM agents can outperform crowdsourced human annotators - on a set of ~16k individual facts, SAFE agrees with crowdsourced human annotators 72% of the time, and on a random subset of 100 disagreement cases, SAFE wins 76% of the time. At the same time, SAFE is more than 20 times cheaper than human annotators. We also benchmark thirteen language models on LongFact across four model families (Gemini, GPT, Claude, and PaLM-2), finding that larger language models generally achieve better long-form factuality. LongFact, SAFE, and all experimental code are available at https://github.com/google-deepmind/long-form-factuality.

Fake news significantly influence our society. They impact consumers, voters, and many other societal groups. While Fake News exist for a centuries, Generative AI brings fake news on a new level. It is now possible to automate the creation of masses of high-quality individually targeted Fake News. On the other end, Generative AI can also help detecting Fake News. Both fields are young but developing fast. This survey provides a comprehensive examination of the research and practical use of Generative AI for Fake News detection and creation in 2024. Following the Structured Literature Survey approach, the paper synthesizes current results in the following topic clusters 1) enabling technologies, 2) creation of Fake News, 3) case study social media as most relevant distribution channel, 4) detection of Fake News, and 5) deepfakes as upcoming technology. The article also identifies current challenges and open issues.

Harmful and offensive communication or content is detrimental to social bonding and the mental state of users on social media platforms. Text detoxification is a crucial task in natural language processing (NLP), where the goal is removing profanity and toxicity from text while preserving its content. Supervised and unsupervised learning are common approaches for designing text detoxification solutions. However, these methods necessitate fine-tuning, leading to computational overhead. In this paper, we propose GPT-DETOX as a framework for prompt-based in-context learning for text detoxification using GPT-3.5 Turbo. We utilize zero-shot and few-shot prompting techniques for detoxifying input sentences. To generate few-shot prompts, we propose two methods: word-matching example selection (WMES) and context-matching example selection (CMES). We additionally take into account ensemble in-context learning (EICL) where the ensemble is shaped by base prompts from zero-shot and all few-shot settings. We use ParaDetox and APPDIA as benchmark detoxification datasets. Our experimental results show that the zero-shot solution achieves promising performance, while our best few-shot setting outperforms the state-of-the-art models on ParaDetox and shows comparable results on APPDIA. Our EICL solutions obtain the greatest performance, adding at least 10% improvement, against both datasets.

Recommender systems (RSs) are designed to provide personalized recommendations to users. Recently, knowledge graphs (KGs) have been widely introduced in RSs to improve recommendation accuracy. In this study, however, we demonstrate that RSs do not necessarily perform worse even if the KG is downgraded to the user-item interaction graph only (or removed). We propose an evaluation framework KG4RecEval to systematically evaluate how much a KG contributes to the recommendation accuracy of a KG-based RS, using our defined metric KGER (KG utilization efficiency in recommendation). We consider the scenarios where knowledge in a KG gets completely removed, randomly distorted and decreased, and also where recommendations are for cold-start users. Our extensive experiments on four commonly used datasets and a number of state-of-the-art KG-based RSs reveal that: to remove, randomly distort or decrease knowledge does not necessarily decrease recommendation accuracy, even for cold-start users. These findings inspire us to rethink how to better utilize knowledge from existing KGs, whereby we discuss and provide insights into what characteristics of datasets and KG-based RSs may help improve KG utilization efficiency.

In recent years, the emergence of models capable of generating images from text has attracted considerable interest, offering the possibility of creating realistic images from text descriptions. Yet these advances have also raised concerns about the potential misuse of these images, including the creation of misleading content such as fake news and propaganda. This study investigates the effectiveness of using advanced vision-language models (VLMs) for synthetic image identification. Specifically, the focus is on tuning state-of-the-art image captioning models for synthetic image detection. By harnessing the robust understanding capabilities of large VLMs, the aim is to distinguish authentic images from synthetic images produced by diffusion-based models. This study contributes to the advancement of synthetic image detection by exploiting the capabilities of visual language models such as BLIP-2 and ViTGPT2. By tailoring image captioning models, we address the challenges associated with the potential misuse of synthetic images in real-world applications. Results described in this paper highlight the promising role of VLMs in the field of synthetic image detection, outperforming conventional image-based detection techniques. Code and models can be found at https://github.com/Mamadou-Keita/VLM-DETECT.

Language Models (LMs) have shown promising performance in natural language generation. However, as LMs often generate incorrect or hallucinated responses, it is crucial to correctly quantify their uncertainty in responding to given inputs. In addition to verbalized confidence elicited via prompting, many uncertainty measures ($e.g.$, semantic entropy and affinity-graph-based measures) have been proposed. However, these measures can differ greatly, and it is unclear how to compare them, partly because they take values over different ranges ($e.g.$, $[0,\infty)$ or $[0,1]$). In this work, we address this issue by developing a novel and practical framework, termed $Rank$-$Calibration$, to assess uncertainty and confidence measures for LMs. Our key tenet is that higher uncertainty (or lower confidence) should imply lower generation quality, on average. Rank-calibration quantifies deviations from this ideal relationship in a principled manner, without requiring ad hoc binary thresholding of the correctness score ($e.g.$, ROUGE or METEOR). The broad applicability and the granular interpretability of our methods are demonstrated empirically.

AI expert Marva Bailer explains how, even though there are currently laws in place, the average person has more access than ever to create deepfakes of celebrities. Katy Perry and Miranda Lambert are just some of the more than over 200 names who have signed a letter speaking out for musicians' rights as artificial intelligence continues to expand its reach. "We, the undersigned members of the artist and songwriting communities, call on AI developers, technology companies, platforms and digital music services to cease the use of artificial intelligence (AI) to infringe upon and devalue the rights of human artists," the letter begins. It goes on to state, "When used irresponsibly, AI poses enormous threats to our ability to protect our privacy, our identities, our music and our livelihoods. Some of the biggest and most powerful companies are, without permission, using our work to train AI models. These efforts are directly aimed at replacing the work of human artists with massive quantities of AI-created'sounds' and'images' that substantially dilute the royalty pools that are paid out to artists. "For many working musicians, artists, and songwriters who are just trying to make ends meet, this would be catastrophic." Katy Perry and Miranda Lambert are just some of the over 200 names signing an open letter asking AI developers to respect artists' rights. WHAT IS ARTIFICIAL INTELLIGENCE (AI)? The open letter, submitted by the Artist Rights Alliance, a non-profit made up of "working musicians, performers, and songwriters fighting for a healthy creative economy and fair treatment for all creators in the digital world," per their official website. The letter notes that "AI has enormous potential to advance human creativity" when used "responsibly." "This assault on human creativity must be stopped.