Johnson spends millions every year in order to find a way to make his organs similar to that of an 18-year-old male. 'IT'S HUMANS I FEAR': Tech billionaire on journey to immortality welcomes AI as a solution. NO PRESSURE: IG report calls on VA to fix automated system that led to faulty claims decisions. Radiologist Bhavik Patel, M.D. (pictured here) has been named chief AI officer at Mayo Clinic Arizona. Businessman chatting through chatbot Online customer service with chat bots for support.

FOX News' Jennifer Griffin reports the latest on the Israel-Hamas war. Sirens are sounding off in northern Israel on Wednesday and residents are being told to shelter in place amid reports of an incoming "large-scale" drone attack. Israeli Defense Forces said it received a report of a "suspected infiltration" from Lebanon into Israeli airspace. "All residents in the areas where the warning was sounded are asked to enter the protected areas and stay in them until further notice," IDF said. "Israelis throughout the country were ordered to shelter in place amid a large-scale attack involving drones coming from the direction of the northern border on Wednesday evening," the Jerusalem Post reported.

At first glance at this video, you'd be forgiven for mistaking it as a clip from Wall-E. But the robot depicted in the footage isn't science fiction - it's very much real. In a video posted by Walt Disney Imagineering, a newly designed bipedal robot walks, struts, dances, and emotes in an impressive display of engineering prowess. The bot also shows off its human interaction skills as it reacts to those around it and even walks behind two children pulling it on a lead. With its expressive head and wiggly antenna, the unnamed robot has been designed to bring the creative designs of animators into the real world using machine learning.

The proliferation of social media platforms such as Twitter, Instagram, and Weibo has significantly enhanced the dissemination of false information. This phenomenon grants both individuals and governmental entities the ability to shape public opinions, highlighting the need for deploying effective detection methods. In this paper, we propose GraMuFeN, a model designed to detect fake content by analyzing both the textual and image content of news. GraMuFeN comprises two primary components: a text encoder and an image encoder. For textual analysis, GraMuFeN treats each text as a graph and employs a Graph Convolutional Neural Network (GCN) as the text encoder. Additionally, the pre-trained ResNet-152, as a Convolutional Neural Network (CNN), has been utilized as the image encoder. By integrating the outputs from these two encoders and implementing a contrastive similarity loss function, GraMuFeN achieves remarkable results. Extensive evaluations conducted on two publicly available benchmark datasets for social media news indicate a 10 % increase in micro F1-Score, signifying improvement over existing state-of-the-art models. These findings underscore the effectiveness of combining GCN and CNN models for detecting fake news in multi-modal data, all while minimizing the additional computational burden imposed by model parameters.

This article presents the affordances that Generative Artificial Intelligence can have in disinformation context, one of the major threats to our digitalized society. We present a research framework to generate customized agent-based social networks for disinformation simulations that would enable understanding and evaluation of the phenomena whilst discussing open challenges.

The detection of state-sponsored trolls operating in influence campaigns on social media is a critical and unsolved challenge for the research community, which has significant implications beyond the online realm. To address this challenge, we propose a new AI-based solution that identifies troll accounts solely through behavioral cues associated with their sequences of sharing activity, encompassing both their actions and the feedback they receive from others. Our approach does not incorporate any textual content shared and consists of two steps: First, we leverage an LSTM-based classifier to determine whether account sequences belong to a state-sponsored troll or an organic, legitimate user. Second, we employ the classified sequences to calculate a metric named the "Troll Score", quantifying the degree to which an account exhibits troll-like behavior. To assess the effectiveness of our method, we examine its performance in the context of the 2016 Russian interference campaign during the U.S. Presidential election. Our experiments yield compelling results, demonstrating that our approach can identify account sequences with an AUC close to 99% and accurately differentiate between Russian trolls and organic users with an AUC of 91%. Notably, our behavioral-based approach holds a significant advantage in the ever-evolving landscape, where textual and linguistic properties can be easily mimicked by Large Language Models (LLMs): In contrast to existing language-based techniques, it relies on more challenging-to-replicate behavioral cues, ensuring greater resilience in identifying influence campaigns, especially given the potential increase in the usage of LLMs for generating inauthentic content. Finally, we assessed the generalizability of our solution to various entities driving different information operations and found promising results that will guide future research.

Kurt "They Cyberguy" Knutsson explains easier ways get Siri to listen to you on your iPhone. Do you ever wish that you could activate Siri without having to say "Hey" first every time? Our favorite virtual assistant on Apple devices got a much-needed upgrade with iOS 17. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK TIPS, TECH REVIEWS, AND EASY HOW-TO'S TO MAKE YOU SMARTER How to use Siri without saying "Hey" with iOS 17 update -safari () With iOS 17, you can also now finally make back-to-back requests without needing to say "Siri" again and again when you want something. How to use Siri without saying "Hey" with iOS 17 update -safari () While we're calling out to Siri, here is a useful new tip to read an article out loud for you that you can ask her to do.

After Disney releases a new film in English, the company will go back and localize it in as many as 46 global languages to make the movie accesible to as wide an audience as possible. This is a massive undertaking, one for which Disney has an entire division -- Disney Character Voices International Inc -- to handle the task. And it's not like you're getting Chris Pratt back in the recording booth to dub his GotG III lines in Icelandic and Swahili -- each version sounds a little different given the local voice actors. But with a new "AI dubbing" system from ElevenLabs, we could soon get a close recreation of Pratt's voice, regardless of the language spoken on-screen. ElevenLabs is an AI startup that offers a voice cloning service, allowing subscribers to generate nearly identical vocalizations with AI based on a few minutes worth of audio sample uploads.

Text-driven diffusion models have exhibited impressive generative capabilities, enabling various image editing tasks. In this paper, we propose TF-ICON, a novel Training-Free Image COmpositioN framework that harnesses the power of text-driven diffusion models for cross-domain image-guided composition. This task aims to seamlessly integrate user-provided objects into a specific visual context. Current diffusion-based methods often involve costly instance-based optimization or finetuning of pretrained models on customized datasets, which can potentially undermine their rich prior. In contrast, TF-ICON can leverage off-the-shelf diffusion models to perform cross-domain image-guided composition without requiring additional training, finetuning, or optimization. Moreover, we introduce the exceptional prompt, which contains no information, to facilitate text-driven diffusion models in accurately inverting real images into latent representations, forming the basis for compositing. Our experiments show that equipping Stable Diffusion with the exceptional prompt outperforms state-of-the-art inversion methods on various datasets (CelebA-HQ, COCO, and ImageNet), and that TF-ICON surpasses prior baselines in versatile visual domains. Code is available at https://github.com/Shilin-LU/TF-ICON

To increase revenue, news websites often resort to using deceptive news titles, luring users into clicking on the title and reading the full news. Clickbait detection is the task that aims to automatically detect this form of false advertisement and avoid wasting the precious time of online users. Despite the importance of the task, to the best of our knowledge, there is no publicly available clickbait corpus for the Romanian language. To this end, we introduce a novel Romanian Clickbait Corpus (RoCliCo) comprising 8,313 news samples which are manually annotated with clickbait and non-clickbait labels. Furthermore, we conduct experiments with four machine learning methods, ranging from handcrafted models to recurrent and transformer-based neural networks, to establish a line-up of competitive baselines. We also carry out experiments with a weighted voting ensemble. Among the considered baselines, we propose a novel BERT-based contrastive learning model that learns to encode news titles and contents into a deep metric space such that titles and contents of non-clickbait news have high cosine similarity, while titles and contents of clickbait news have low cosine similarity. Our data set and code to reproduce the baselines are publicly available for download at https://github.com/dariabroscoteanu/RoCliCo.