Text-to-image diffusion models are nothing but a revolution, allowing anyone, even without design skills, to create realistic images from simple text inputs. With powerful personalization tools like DreamBooth, they can generate images of a specific person just by learning from his/her few reference images. However, when misused, such a powerful and convenient tool can produce fake news or disturbing content targeting any individual victim, posing a severe negative social impact. In this paper, we explore a defense system called Anti-DreamBooth against such malicious use of DreamBooth. The system aims to add subtle noise perturbation to each user's image before publishing in order to disrupt the generation quality of any DreamBooth model trained on these perturbed images. We investigate a wide range of algorithms for perturbation optimization and extensively evaluate them on two facial datasets over various text-to-image model versions. Despite the complicated formulation of DreamBooth and Diffusion-based text-to-image models, our methods effectively defend users from the malicious use of those models. Their effectiveness withstands even adverse conditions, such as model or prompt/term mismatching between training and testing. Our code will be available at https://github.com/VinAIResearch/Anti-DreamBooth.git.

Knowledge graph completion (KGC) aims to discover missing relations of query entities. Current text-based models utilize the entity name and description to infer the tail entity given the head entity and a certain relation. Existing approaches also consider the neighborhood of the head entity. However, these methods tend to model the neighborhood using a flat structure and are only restricted to 1-hop neighbors. In this work, we propose a node neighborhood-enhanced framework for knowledge graph completion. It models the head entity neighborhood from multiple hops using graph neural networks to enrich the head node information. Moreover, we introduce an additional edge link prediction task to improve KGC. Evaluation on two public datasets shows that this framework is simple yet effective. The case study also shows that the model is able to predict explainable predictions.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A new study found that employees are more likely to be lazy when working near or with robots versus working on their own or with a group of people. Scientists at the Technical University of Berlin in Germany provided images of circuit boards to 42 employees in which the boards were blurred. Sharpened images could only be viewed by holding a mouse tool over them, according to the study published in the journal Frontiers in Robotics and AI.

Creating music is iterative, requiring varied methods at each stage. However, existing AI music systems fall short in orchestrating multiple subsystems for diverse needs. To address this gap, we introduce Loop Copilot, a novel system that enables users to generate and iteratively refine music through an interactive, multi-round dialogue interface. The system uses a large language model to interpret user intentions and select appropriate AI models for task execution. Each backend model is specialized for a specific task, and their outputs are aggregated to meet the user's requirements. To ensure musical coherence, essential attributes are maintained in a centralized table. We evaluate the effectiveness of the proposed system through semi-structured interviews and questionnaires, highlighting its utility not only in facilitating music creation but also its potential for broader applications.

By now the sophistication of false information about Israel and Hamas is clear to anyone who opened their phone this week. As tech platforms rely ever more on artificial intelligence in their battle against disinformation, the havoc in the Middle East exposes the limits of technology to police technology's harms. It is more important than ever that we understand how global platforms like Meta, Google, and X, the platform formerly known as Twitter, make decisions about what content gets amplified and what taken down. It's not as though platforms didn't know they had a huge disinformation problem that human content moderators alone could not solve. Two years ago, Facebook whistleblower Frances Haugen detailed for Congress how growth and profit drove decisions: "The result has been more division, more harm, more lies, more threats and more combat," she testified.

CyberGuy goes over the features of the smart glasses released by Meta and Ray-Ban. You might think that sunglasses are just for blocking the sun or making a fashion statement. That's not what Mark Zuckerberg envisions with the relaunch of Meta's Ray-Ban smart glasses. The previous smart glasses version from the joint venture was a flop, failing to gain broad consumer traction. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK VIDEO TIPS, TECH REVIEWS, AND EASY HOW-TO'S TO MAKE YOU SMARTER Zuckerberg is not giving up on his vision of creating a wearable device that can do more than just capture and share photos and videos.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Pras Michel of the Fugees is seeking a new trial by arguing his former lawyer used artificial intelligence to generate his closing argument before the hip-hop artist was found guilty of helping a foreign national launder millions of dollars in illegitimate contributions to former President Barack Obama's campaign. Michel was convicted in April after being accused of taking part in an extensive conspiracy to use about $88 million in foreign funds to engage in illegal back-channel lobbying and make unlawful campaign contributions at the direction of the People's Republic of China. He filed a motion on Monday asking the court for a new trial on all counts.

Advances in artificial intelligence could have'civilisation-altering impacts' and rapidly increase the amount of dangerous misinformation being spread online, a former Facebook employee has warned. Whistleblower Frances Haugen said as AI became bigger and economies relied more on software running on data centres the world would start to see an'era of opacity' creep in. The former engineer and product manager - who quit Facebook in 2021 after leaking thousands of documents showing toxic content was being spread knowingly by the platform - said without stronger regulation there would be'a repeat of what we saw with social media' on a far greater scale. 'When we start getting into scalable systems that run on data centres, a very small number of people can have civilisation-impacting levels of power,' Ms Haugen told the National Press Club on Tuesday. 'At Facebook, there's a very small number of people who really understand how these algorithms work and yet it impacts what everyone sees in the news.

This paper is devoted to the development of a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models. Significant concerns emerge when addressing cultural sensitivity and local values. To address this, the paper proposes a comprehensive solution that includes further pre-training with Arabic texts, Supervised Fine-Tuning (SFT) utilizing native Arabic instructions, and GPT-4 responses in Arabic, alongside Reinforcement Learning with AI Feedback (RLAIF) employing a reward model attuned to local culture and values. The goal is to cultivate culturally cognizant and value-aligned Arabic LLMs capable of accommodating the diverse, application-specific needs of Arabic-speaking communities. Comprehensive evaluations reveal that the resulting model, dubbed 'AceGPT', sets the state-of-the-art standard for open Arabic LLMs across various benchmarks, including the instruction-following benchmark (i.e., Arabic Vicuna-80 and Arabic AlpacaEval), knowledge benchmark (i.e., Arabic MMLU and EXAMs), and the newly introduced Arabic Cultural and Value Alignment benchmark. Notably, AceGPT outperforms Turbo in the popular Vicuna-80 benchmark when evaluated with GPT-4, despite the benchmark's limited scale. Codes, data, and models are in https://github.com/FreedomIntelligence/AceGPT.

Media bias has been extensively studied by both social and computational sciences. However, current work still has a large reliance on human input and subjective assessment to label biases. This is especially true for cable news research. To address these issues, we develop an unsupervised machine learning method to characterize the bias of cable news programs without any human input. This method relies on the analysis of what topics are mentioned through Named Entity Recognition and how those topics are discussed through Stance Analysis in order to cluster programs with similar biases together. Applying our method to 2020 cable news transcripts, we find that program clusters are consistent over time and roughly correspond to the cable news network of the program. This method reveals the potential for future tools to objectively assess media bias and characterize unfamiliar media environments.