Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Bad weather Tuesday was hampering the search for two men who went over a waterfall while fishing in the Boundary Waters Canoe Area Wilderness of northern Minnesota over the weekend. Nate Skelton told the Star Tribune of Minneapolis that the cloud cover was too low for aerial surveillance and up to 2 inches of rain was anticipated, so the next two days were not promising. Skelton said a search crew was camping on site, waiting for conditions to improve in the remote area, about 100 miles north of Duluth.

Scarlett Johannson has gone to war with OpenAI, and in the battle for public opinion, OpenAI is losing--badly. Last week, OpenAI released an update of its AI chatbot called ChatGPT-4o, which featured a female voice talking to its users. Many people pointed out that the voice, which sometimes seemed to veer into flirtation, was eerily similar to Scarlett Johannson's in the 2013 dystopian sci-fi film Her. OpenAI CEO Sam Altman has long talked about how much the movie inspired the company's products, and even made the connection clear last week by tweeting the title of the movie. But on Monday, Johannson released a statement saying OpenAI had asked her to be the voice of the chatbot, and when she refused, they found a soundalike.

Celebrity break-ups have impacted fans for years, leaving them questioning what the couple's future would have looked like if they stayed together. Now a graphic designer has brought the'what-ifs' to life using AI, revealing the families of famous ex-couples like Brad Pitt and Jennifer Aniston, and Twilight stars Robert Pattinson and Kristen Stewart. Jeremy Pomeroy also recreated the romance of Taylor Swift and Harry Styles, who separated in 2013, generating a little girl and boy who look like the Eras Tour star. The Australian designer used photoshop and advanced AI algorithms to'help generate unique and imaginative images based on existing data and my guidance.' Taylor Swift and Harry Styles (pictured) dated briefly from late 2012 to early 2013 and reportedly broke up due to long-distance.

Ever since Scarlett Johansson voiced an AI assistant in the sci-fi blockbuster'Her', many tech fans have dreamed of making that technology a reality. But it now seems that OpenAI may have pursued that dream too literally as they face accusations of deliberately copying Johansson's voice for ChatGPT's latest update. According to Ms Johansson's statement, the likeness is'so eerily similar to mine that close friends and news outlets could not tell the difference'. Following the allegations, OpenAI's'flirty' voice assistant has now been paused, yet tech fans have been weighing in on whether there really is a resemblance. So, do you think ChatGPT's AI voice sounds like Scarlett Johansson?

'The CyberGuy' Kurt Knutsson joins'Fox & Friends Weekend' to discuss Elon Musk's lawsuit against OpenAI and its CEO over a contractual breach, saying hes right on this one. "Avengers" and "Her" actress Scarlett Johansson revealed that legal action was likely behind OpenAI removing a voice that sounded eerily like hers. A statement released by NPR on Monday explained that OpenAI CEO Sam Altman reached out to Johansson in September about possibly hiring her to voice the ChatGPT 4.0 system. She claimed he suggested her "comforting" voice "could bridge the gap between tech companies and creatives" and help with the "seismic shift concerning humans and Al." Though she rejected the offer after "much consideration and for personal reasons," Johansson was furious to hear the public discuss how the "Sky" voice system resembled hers. Scarlett Johansson said in a statement that she took legal action against OpenAI CEO Sam Altman and the company.

We present BYOKG, a universal question-answering (QA) system that can operate on any knowledge graph (KG), requires no human-annotated training data, and can be ready to use within a day -- attributes that are out-of-scope for current KGQA systems. BYOKG draws inspiration from the remarkable ability of humans to comprehend information present in an unseen KG through exploration -- starting at random nodes, inspecting the labels of adjacent nodes and edges, and combining them with their prior world knowledge. In BYOKG, exploration leverages an LLM-backed symbolic agent that generates a diverse set of query-program exemplars, which are then used to ground a retrieval-augmented reasoning procedure to predict programs for arbitrary questions. BYOKG is effective over both small- and large-scale graphs, showing dramatic gains in QA accuracy over a zero-shot baseline of 27.89 and 58.02 F1 on GrailQA and MetaQA, respectively. On GrailQA, we further show that our unsupervised BYOKG outperforms a supervised in-context learning method, demonstrating the effectiveness of exploration. Lastly, we find that performance of BYOKG reliably improves with continued exploration as well as improvements in the base LLM, notably outperforming a state-of-the-art fine-tuned model by 7.08 F1 on a sub-sampled zero-shot split of GrailQA.

In the current era of digital communication and widespread use of social media, it is crucial to develop an understanding of persuasive techniques employed in written text. This knowledge is essential for effectively discerning accurate information and making informed decisions. To address this need, this paper presents a comprehensive empirical study focused on identifying persuasive techniques in Arabic social media content. To achieve this objective, we utilize Pre-trained Language Models (PLMs) and leverage the ArAlEval dataset, which encompasses two tasks: binary classification to determine the presence or absence of persuasion techniques, and multi-label classification to identify the specific types of techniques employed in the text. Our study explores three different learning approaches by harnessing the power of PLMs: feature extraction, fine-tuning, and prompt engineering techniques. Through extensive experimentation, we find that the fine-tuning approach yields the highest results on the aforementioned dataset, achieving an f1-micro score of 0.865 and an f1-weighted score of 0.861. Furthermore, our analysis sheds light on an interesting finding. While the performance of the GPT model is relatively lower compared to the other approaches, we have observed that by employing few-shot learning techniques, we can enhance its results by up to 20\%. This offers promising directions for future research and exploration in this topic\footnote{Upon Acceptance, the source code will be released on GitHub.}.

We present a new approach for fast and controllable generation of symbolic music based on the simplex diffusion, which is essentially a diffusion process operating on probabilities rather than the signal space. This objective has been applied in domains such as natural language processing but here we apply it to generating 4-bar multi-instrument music loops using an orderless representation. We show that our model can be steered with vocabulary priors, which affords a considerable level control over the music generation process, for instance, infilling in time and pitch and choice of instrumentation -- all without task-specific model adaptation or applying extrinsic control.

Nowadays, humans are constantly exposed to music, whether through voluntary streaming services or incidental encounters during commercial breaks. Despite the abundance of music, certain pieces remain more memorable and often gain greater popularity. Inspired by this phenomenon, we focus on measuring and predicting music memorability. To achieve this, we collect a new music piece dataset with reliable memorability labels using a novel interactive experimental procedure. We then train baselines to predict and analyze music memorability, leveraging both interpretable features and audio mel-spectrograms as inputs. To the best of our knowledge, we are the first to explore music memorability using data-driven deep learning-based methods. Through a series of experiments and ablation studies, we demonstrate that while there is room for improvement, predicting music memorability with limited data is possible. Certain intrinsic elements, such as higher valence, arousal, and faster tempo, contribute to memorable music. As prediction techniques continue to evolve, real-life applications like music recommendation systems and music style transfer will undoubtedly benefit from this new area of research.

Large Language Models (LLMs) have revolutionised the field of Natural Language Processing (NLP) and have achieved state-of-the-art performance in practically every task in this field. However, the prevalent approach used in text generation, Causal Language Modelling (CLM), which generates text sequentially from left to right, inherently limits the freedom of the model, which does not decide when and where each token is generated. In contrast, Masked Language Modelling (MLM), primarily used for language understanding tasks, can generate tokens anywhere in the text and any order. This paper conducts an extensive comparison of MLM and CLM approaches for text generation tasks. To do so, we pre-train several language models of comparable sizes on three different datasets, namely 1) medical discharge summaries, 2) movie plot synopses, and 3) authorship verification datasets. To assess the quality of the generations, we first employ quantitative metrics and then perform a qualitative human evaluation to analyse coherence and grammatical correctness. In addition, we evaluate the usefulness of the generated texts by using them in three different downstream tasks: 1) Entity Recognition, 2) Text Classification, and 3) Authorship Verification. The results show that MLM consistently outperforms CLM in text generation across all datasets, with higher quantitative scores and better coherence in the generated text. The study also finds \textit{no strong correlation} between the quality of the generated text and the performance of the models in the downstream tasks. With this study, we show that MLM for text generation has great potential for future research and provides direction for future studies in this area.