The accelerated dissemination of disinformation often outpaces the capacity for manual fact-checking, highlighting the urgent need for Semi-Automated Fact-Checking (SAFC) systems. Within the Portuguese language context, there is a noted scarcity of publicly available datasets ( corpora) that integrate external evidence, an essential component for developing robust AFC systems, as many existing resources focus solely on classification based on intrinsic text features. This dissertation addresses this gap by developing, applying, and analyzing a methodology to enrich Portuguese news corpora (Fake.Br, COVID19.BR, MuMiN-PT) with external evidence. The approach simulates a user's verification process, employing Large Language Models (LLMs, specifically Gemini 1.5 Flash) to extract the main claim from texts and search engine APIs (Google Search API, Google FactCheck Claims Search API) to retrieve relevant external documents (evidence). Additionally, a data validation and pre-processing framework, including near-duplicate detection, is introduced to enhance the quality of the base corpora. The main results demonstrate the methodology's viability, providing enriched corpora and analyses that confirm the utility of claim extraction, the influence of original data characteristics on the process, and the positive impact of enrichment on the performance of classification models (Bertimbau and Gemini 1.5 Flash), especially with fine-tuning. This work contributes valuable resources and insights for advancing SAFC in Portuguese.

Generating high-quality speech efficiently remains a key challenge for generative models in speech synthesis. This paper introduces VQalAttent, a lightweight model designed to generate fake speech with tunable performance and interpretability. Leveraging the AudioMNIST dataset, consisting of human utterances of decimal digits (0-9), our method employs a two-step architecture: first, a scalable vector quantized autoencoder (VQ-VAE) that compresses audio spectrograms into discrete latent representations, and second, a decoder-only transformer that learns the probability model of these latents. Trained transformer generates similar latent sequences, convertible to audio spectrograms by the VQ-VAE decoder, from which we generate fake utterances. Interpreting statistical and perceptual quality of the fakes, depending on the dimension and the extrinsic information of the latent space, enables guided improvements in larger, commercial generative models. As a valuable tool for understanding and refining audio synthesis, our results demonstrate VQalAttent's capacity to generate intelligible speech samples with limited computational resources, while the modularity and transparency of the training pipeline helps easily correlate the analytics with modular modifications, hence providing insights for the more complex models.

A false post claiming to show a pro-abortion campaign ad by Kamala Harris has been widely circulated on social media. BBC Verify has found that it was made by recycling an old advert for UK department store and adding an AI-generated voice over.

Deep learning currently is used in LLMs (Large Language Models), for image identification, creation of deepfakes and analyses of astrophysical and financial information (Krizhevsky, 2012), (Sutskever, 2014), (Vaswani, 2017), (Wang, 2015), (George, 2018), (Li, 2010). When the instruments of deep learning became widely available, it was decided that the decipherment of all dead languages was only a matter of time (see (Xusen, 2019) and op.

In this work, we investigate to use Large Language Models (LLMs) for rumor detection on social media. However, it is challenging for LLMs to reason over the entire propagation information on social media, which contains news contents and numerous comments, due to LLMs may not concentrate on key clues in the complex propagation information, and have trouble in reasoning when facing massive and redundant information. Accordingly, we propose an LLM-empowered Rumor Detection (LeRuD) approach, in which we design prompts to teach LLMs to reason over important clues in news and comments, and divide the entire propagation information into a Chain-of-Propagation for reducing LLMs' burden. We conduct extensive experiments on the Twitter and Weibo datasets, and LeRuD outperforms several state-of-the-art rumor detection models by 3.2% to 7.7%. Meanwhile, by applying LLMs, LeRuD requires no data for training, and thus shows more promising rumor detection ability in few-shot or zero-shot scenarios.

Artificial Intelligence-powered influencers are the new social media trend. But there could be negative impacts from the perfect influencers, a humane technologist warns. INFLUENCER TRAP: New social media trend could prompt mental health crises, suicide as users tune into'fake life': tech founder. WORK WORRIES: A new poll reveals what Americans fear about AI taking their jobs. Republican Wisconsin state Rep. David Steffen has proposed restrictions for minors across all social media platforms.

Image synthesis has seen significant advancements with the advent of diffusion-based generative models like Denoising Diffusion Probabilistic Models (DDPM) and text-to-image diffusion models. Despite their efficacy, there is a dearth of research dedicated to detecting diffusion-generated images, which could pose potential security and privacy risks. This paper addresses this gap by proposing a novel detection method called Stepwise Error for Diffusion-generated Image Detection (SeDID). Comprising statistical-based $\text{SeDID}_{\text{Stat}}$ and neural network-based $\text{SeDID}_{\text{NNs}}$, SeDID exploits the unique attributes of diffusion models, namely deterministic reverse and deterministic denoising computation errors. Our evaluations demonstrate SeDID's superior performance over existing methods when applied to diffusion models. Thus, our work makes a pivotal contribution to distinguishing diffusion model-generated images, marking a significant step in the domain of artificial intelligence security.

It's changing our world, and it seems everyone is talking about it! Artificial Intelligence or AI as it's known, is technology that enables a computer to think or act in a more'human' way, so much so, that distinguishing the fake from the real is becoming more difficult… But have you ever fallen for a fake, AI image or video? Have a go at this quiz and see if you can spot the real from the AI!

In a letter to streamers including Spotify and Apple Music, the record label Universal Music Group expressed fears that AI labs would scrape millions of tracks to use as training data for their models and copycat versions of pop stars. UMG instructed the platforms to block those downloads, saying it would "not hesitate to take steps to protect our rights and those of our artists". The letter, first reported by the Financial Times, comes after a similar move from the Recording Industry Association of America, the industry's trade body, last October. Writing to the US trade representative, the RIAA said that AI-based technology was able "to be very similar to or almost as good as reference tracks by selected, well known sound recording artists". The group added: "To the extent these services, or their partners, are training their AI models using our members' music, that use is unauthorised and infringes our members' rights by making unauthorised copies of our members works."

It is a robot researcher with good communication skills; you can ask it to answer questions about various areas of knowledge and it will write short documents in various formats and in excellent English. Or write bad poetry, incomprehensible jokes, and obey a command like "Write Tetris in C." What comes out looks like it could be, too. Coders love that sort of thing, and have been stuffing Stack Overflow's dev query boards with generated snippets. Just one problem – the quality of the code is bad. So bad, Stack Overflow has screamed "STOP!" and is mulling general guidelines to stop it happening again.