According to his profile, Max, a contestant on season six of the Netflix reality show The Circle, is 26 years old, brunette and into his Australian shepherd, Pippa. He is a veterinary intern from Pismo Beach, California, and a bit cheeky – "single, but my dog is taken". He enters into the Circle chat, the fake social media service contestants use to vie for 100,000, posting either as themselves, an embellished version of themselves or a fully fake identity, with ease. He seems so real," says Lauren, a fellow twentysomething hoping to build enough online alliances and secure enough positive peer reviews to win, upon seeing Max's profile. You just know the producers ate that up, because "Max" is the front for an AI chatbot, a new gimmick to up the ante in this middleweight reality show. The Circle has nowhere near the following of Love Island, but hasn't sunk to the bottom of the streaming service slush pile – and is the latest example of artificial intelligence's seemingly inexorable ...

Nowadays, Information spreads at an unprecedented pace in social media and discerning truth from misinformation and fake news has become an acute societal challenge. Machine learning (ML) models have been employed to identify fake news but are far from perfect with challenging problems like limited accuracy, interpretability, and generalizability. In this paper, we enhance ML-based solutions with linguistics input and we propose LingML, linguistic-informed ML, for fake news detection. We conducted an experimental study with a popular dataset on fake news during the pandemic. The experiment results show that our proposed solution is highly effective. There are fewer than two errors out of every ten attempts with only linguistic input used in ML and the knowledge is highly explainable. When linguistics input is integrated with advanced large-scale ML models for natural language processing, our solution outperforms existing ones with 1.8% average error rate. LingML creates a new path with linguistics to push the frontier of effective and efficient fake news detection. It also sheds light on real-world multi-disciplinary applications requiring both ML and domain expertise to achieve optimal performance.

In the contemporary era characterized by a deluge of data, the intelligence community faces the challenge of information overload, needing to process vast amounts of information swiftly and effectively. The ability to generate succinct, clear, and actionable summaries from diverse data sources is crucial, as it often determines the success of strategic objectives in this information-rich environment. As the demand for systems capable of automating large-scale text summarization without compromising on quality or relevance intensifies, the role of such technologies becomes increasingly critical Liu and Lapata [2019]. Text summarization, a pivotal task within Natural Language Processing (NLP), has found widespread application across various domains, including news aggregation and the distillation of extensive documents into manageable summaries. The exponential growth in data underscores the utility of text summarization in enhancing content accessibility and comprehension, thus facilitating more efficient navigation through information landscapes Chouikhi and Alsuhaibani [2022].

We introduce Vidu, a high-performance text-to-video generator that is capable of producing 1080p videos up to 16 seconds in a single generation. Vidu is a diffusion model with U-ViT as its backbone, which unlocks the scalability and the capability for handling long videos. Vidu exhibits strong coherence and dynamism, and is capable of generating both realistic and imaginative videos, as well as understanding some professional photography techniques, on par with Sora -- the most powerful reported text-to-video generator. Finally, we perform initial experiments on other controllable video generation, including canny-to-video generation, video prediction and subject-driven generation, which demonstrate promising results.

In recent years, AI-Generated Content (AIGC) has witnessed rapid advancements, facilitating the generation of music, images, and other forms of artistic expression across various industries. However, researches on general multi-modal music generation model remain scarce. To fill this gap, we propose a multi-modal music generation framework Mozart's Touch. It could generate aligned music with the cross-modality inputs, such as images, videos and text. Mozart's Touch is composed of three main components: Multi-modal Captioning Module, Large Language Model (LLM) Understanding & Bridging Module, and Music Generation Module. Unlike traditional approaches, Mozart's Touch requires no training or fine-tuning pre-trained models, offering efficiency and transparency through clear, interpretable prompts. We also introduce "LLM-Bridge" method to resolve the heterogeneous representation problems between descriptive texts of different modalities. We conduct a series of objective and subjective evaluations on the proposed model, and results indicate that our model surpasses the performance of current state-of-the-art models. Our codes and examples is availble at: https://github.com/WangTooNaive/MozartsTouch

More than anything, Star Wars: Episode 1 - The Phantom Menace is a fascinating cultural object. It's been 25 years since I saw the film in theaters, and over a decade since I last rewatched it (in a vain attempt to help my Trekkie wife catch up to the prequels). I've had enough time to process the initial disappointment and embarrassment of introducing my wife to Jar Jar Binks. So when Disney announced it was bringing the prequel trilogy back to theaters, I was practically giddy about revisiting them to see how George Lucas's final films compared to the onslaught of Star Wars media we've experienced over the past decade. Was The Phantom Menace as bad as I'd remembered?

Drake and Kendrick Lamar's ongoing beef has left their devoted fans utterly divided over who's winning. The rappers have released several diss songs against each other and their fan bases are certain their team is winning. To try and strip biases out of the debate, we asked artificial intelligence chatbots who is winning the ongoing feud - and it produced some surprising insights. Three out of four AI chatbots remained politically correct when addressing which rapper is winning the beef, calling it'subjective' and saying it is up to the fans to decide. But Meta's AI bot said Kendrick had a slight edge in the beef so far. Gemini called Drake a'commercial powerhouse with numerous hit singles and albums that have topped the charts,' but said who is winning the feud remains subjective'Ultimately, the winner of the Drake versus Kendrick Lamar beef is subjective and depends on personal preference,' said DeepAI.

When the system detects small cracks in road surfaces, it promptly seals them. The United States will suffer in the race to command the development of artificial intelligence (AI) if production and manufacture of semiconductor chips and processors remain offshore, according to an industry expert. "If you're not making things and all you're doing is designing the software, and maybe designing the chips, but they're completely built and packaged elsewhere, you don't end up innovating as much when you literally have people's hands making some of these technologies," Jonathan Klamkin, CEO of semiconductor company Aeluma, told Fox News Digital. "You innovate across the supply chain, you'll innovate the manufacturing equipment that's used in the pads, you'll innovate how to operate the pads, you'll innovate the design of the chips," Klamkin said. "The U.S. needs to be vertically integrated in semiconductors. We can't just design the chips and write the software code."

Adversarial information operations can destabilize societies by undermining fair elections, manipulating public opinions on policies, and promoting scams. Despite their widespread occurrence and potential impacts, our understanding of influence campaigns is limited by manual analysis of messages and subjective interpretation of their observable behavior. In this paper, we explore whether these limitations can be mitigated with large language models (LLMs), using GPT-3.5 as a case-study for coordinated campaign annotation. We first use GPT-3.5 to scrutinize 126 identified information operations spanning over a decade. We utilize a number of metrics to quantify the close (if imperfect) agreement between LLM and ground truth descriptions. We next extract coordinated campaigns from two large multilingual datasets from X (formerly Twitter) that respectively discuss the 2022 French election and 2023 Balikaran Philippine-U.S. military exercise in 2023. For each coordinated campaign, we use GPT-3.5 to analyze posts related to a specific concern and extract goals, tactics, and narrative frames, both before and after critical events (such as the date of an election). While the GPT-3.5 sometimes disagrees with subjective interpretation, its ability to summarize and interpret demonstrates LLMs' potential to extract higher-order indicators from text to provide a more complete picture of the information campaigns compared to previous methods.

Deception, a prevalent aspect of human communication, has undergone a significant transformation in the digital age. With the globalization of online interactions, individuals are communicating in multiple languages and mixing languages on social media, with varied data becoming available in each language and dialect. At the same time, the techniques for detecting deception are similar across the board. Recent studies have shown the possibility of the existence of universal linguistic cues to deception across domains within the English language; however, the existence of such cues in other languages remains unknown. Furthermore, the practical task of deception detection in low-resource languages is not a well-studied problem due to the lack of labeled data. Another dimension of deception is multimodality. For example, a picture with an altered caption in fake news or disinformation may exist. This paper calls for a comprehensive investigation into the complexities of deceptive language across linguistic boundaries and modalities within the realm of computer security and natural language processing and the possibility of using multilingual transformer models and labeled data in various languages to universally address the task of deception detection.