This week, a new range of Google smartphones capable of AI image generation has been launched. But for an increasing number of people, the appeal of a less cutting-edge piece of equipment is proving hard to resist: the point-and-shoot camera. The US footballer Megan Rapinoe was seen snapping from the stands at the Paris Olympics. The model Alexa Chung captioned a recent Instagram of her with a camera: "Just another Millennial with a dependency on Snappy Snaps, fighting digital threat with an analogue mode. " A recent glimpse of home life for Rihanna and A AP Rocky showed a disposable camera lying among the clutter.

The landscape of social media content has evolved significantly, extending from text to multimodal formats. This evolution presents a significant challenge in combating misinformation. Previous research has primarily focused on single modalities or text-image combinations, leaving a gap in detecting multimodal misinformation. While the concept of entity consistency holds promise in detecting multimodal misinformation, simplifying the representation to a scalar value overlooks the inherent complexities of high-dimensional representations across different modalities. To address these limitations, we propose a Multimedia Misinformation Detection (MultiMD) framework for detecting misinformation from video content by leveraging cross-modal entity consistency. The proposed dual learning approach allows for not only enhancing misinformation detection performance but also improving representation learning of entity consistency across different modalities. Our results demonstrate that MultiMD outperforms state-of-the-art baseline models and underscore the importance of each modality in misinformation detection. Our research provides novel methodological and technical insights into multimodal misinformation detection.

Despite advancements in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems, their effectiveness is often hindered by a lack of integration with entity relationships and community structures, limiting their ability to provide contextually rich and accurate information retrieval for fact-checking. We introduce CommunityKG-RAG (Community Knowledge Graph-Retrieval Augmented Generation), a novel zero-shot framework that integrates community structures within Knowledge Graphs (KGs) with RAG systems to enhance the fact-checking process. Capable of adapting to new domains and queries without additional training, CommunityKG-RAG utilizes the multi-hop nature of community structures within KGs to significantly improve the accuracy and relevance of information retrieval. Our experimental results demonstrate that CommunityKG-RAG outperforms traditional methods, representing a significant advancement in fact-checking by offering a robust, scalable, and efficient solution.

Dialogue separation involves isolating a dialogue signal from a mixture, such as a movie or a TV program. This can be a necessary step to enable dialogue enhancement for broadcast-related applications. In this paper, ConcateNet for dialogue separation is proposed, which is based on a novel approach for processing local and global features aimed at better generalization for out-of-domain signals. ConcateNet is trained using a noise reduction-focused, publicly available dataset and evaluated using three datasets: two noise reduction-focused datasets (in-domain), which show competitive performance for ConcateNet, and a broadcast-focused dataset (out-of-domain), which verifies the better generalization performance for the proposed architecture compared to considered state-of-the-art noise-reduction methods.

As the US government weighs its options following a landmark "monopolist" ruling against Google last week, online publications increasingly face a bleak future. Bloomberg reports that their choice now boils down to allowing Google to use their published content to produce inline AI-generated search "answers" or losing visibility in the company's search engine. The crux of the problem lies in the Googlebot, the crawler that scours and indexes the live web to produce the results you see when you enter search terms. If publishers block Google from using their content for the AI-produced answers you now see littered at the top of many search results, they also lose the privilege of appearing in other Google search programs like snippets and Discover. Google uses a separate crawler for its Gemini (formerly Bard) chatbot, but its AI Overviews are generated using data from its main crawler.

Anti-vaccine activist Robert F. Kennedy Jr.'s presidential campaign hosted an online panel Wednesday on the future of AI moderated, for some reason, by Ian Carroll, a self-styled journalist with a history of antisemitic statements. In the course of the conversation, Kennedy admitted that he "gets manipulated by AI all the time." "Somebody will send me something and I'll go'Holy cow, did you see this?'," he said, describing how he credulously forwards fake content to his children, only for them to have to correct him. RFK Jr. said he regularly "gets manipulated by AI." While Carroll has no particular public profile on AI, his persona tracks with the campaign's focus on tech figures and influencers as it courts a young, male, and extremely online audience.

A good soundbar is one of the easiest ways to improve the audio quality of your home theater set up. As it happens the Sonos Beam (Gen 2), which is one of our favorite mid-range soundbars, is currently available for its best price to date (at least for a new and not refurbished model). It has dropped by 110 to 389 at Woot. That's 22 percent off the regular price, but bear in mind that the offer only applies to the white version. The Sonos Beam does a bang-up job of delivering solid audio from your TV (or music or podcast service).

This paper develops an agent-based automated fact-checking approach for detecting misinformation. We demonstrate that combining a powerful LLM agent, which does not have access to the internet for searches, with an online web search agent yields better results than when each tool is used independently. Our approach is robust across multiple models, outperforming alternatives and increasing the macro F1 of misinformation detection by as much as 20 percent compared to LLMs without search. We also conduct extensive analyses on the sources our system leverages and their biases, decisions in the construction of the system like the search tool and the knowledge base, the type of evidence needed and its impact on the results, and other parts of the overall process. By combining strong performance with in-depth understanding, we hope to provide building blocks for future search-enabled misinformation mitigation systems.

Image-to-Image translation in Generative Artificial Intelligence (Generative AI) has been a central focus of research, with applications spanning healthcare, remote sensing, physics, chemistry, photography, and more. Among the numerous methodologies, Generative Adversarial Networks (GANs) with contrastive learning have been particularly successful. This study aims to demonstrate that the Kolmogorov-Arnold Network (KAN) can effectively replace the Multi-layer Perceptron (MLP) method in generative AI, particularly in the subdomain of image-to-image translation, to achieve better generative quality. Our novel approach replaces the two-layer MLP with a two-layer KAN in the existing Contrastive Unpaired Image-to-Image Translation (CUT) model, developing the KAN-CUT model. This substitution favors the generation of more informative features in low-dimensional vector representations, which contrastive learning can utilize more effectively to produce high-quality images in the target domain. Extensive experiments, detailed in the results section, demonstrate the applicability of KAN in conjunction with contrastive learning and GANs in Generative AI, particularly for image-to-image translation. This work suggests that KAN could be a valuable component in the broader generative AI domain.

Towards human-level visual understanding, visual commonsense generation has been introduced to generate commonsense inferences beyond images. However, current research on visual commonsense generation has overlooked an important human cognitive ability: generating descriptive and diverse inferences. In this work, we propose a novel visual commonsense generation framework, called DIVE, which aims to improve the descriptiveness and diversity of generated inferences. DIVE involves two methods, generic inference filtering and contrastive retrieval learning, which address the limitations of existing visual commonsense resources and training objectives. Experimental results verify that DIVE outperforms state-of-the-art models for visual commonsense generation in terms of both descriptiveness and diversity, while showing a superior quality in generating unique and novel inferences. Notably, DIVE achieves human-level descriptiveness and diversity on Visual Commonsense Graphs. Furthermore, human evaluations confirm that DIVE aligns closely with human judgments on descriptiveness and diversity\footnote{Our code and dataset are available at https://github.com/Park-ing-lot/DIVE.