The spread of misinformation is a massive problem online, and generative AI is only helping boost the creation of inauthentic or real-but-repurposed media. Even in the pre-generative-AI era, an image surfaced through a quick Google search might have been used out of context or attached to a less-than-reliable website. Google believes it has at least one solution for this problem. In Google image search results, users will start seeing an information box called "About this image." It rolls out today in the US (and initially only in English).

Fox News contributor Dr. Marc Siegel weighs in on how artificial intelligence can change the patient-doctor relationship on "America's Newsroom." A study found that artificial intelligence chatbots such as the popular ChatGPT return common debunked medical stereotypes about Black people. Researchers at Stanford University ran nine medical questions through AI chatbots and found that they returned responses that contained debunked medical claims about Black people, including incorrect responses about kidney function and lung capacity, as well as the notion that Black people have different muscle mass than White people, according to a report from Axios. The team of researchers ran the nine questions through four chatbots, including OpenAI's ChatGPT and Google's Bard, that are trained to scour large amounts of internet text, the report noted, but the responses raised concerns about the growing use of AI in the medical field. A study found that artificial intelligence chatbots such as the popular ChatGPT return common debunked medical stereotypes about Black people.

Adult film icon and media investor Riley Reid aims to bring the transformational capabilities of generative AI to adult entertainment with an online platform where users can chat with digital versions of content creators. But unlike other, scuzzier adult chatbots, Clona.AI's avatars are trained with explicit consent of the models' creators who have direct input in what the "AI companions" will, and won't, talk about. For $30 a month, fans and subscribers will be able to hold "intimate conversations" with digital versions of their favorite adult stars, content creators and influencers. The site's roster currently includes Reid herself and Lena the Plug. A free tier is also available but offers just five chat messages per month. "The reality is, AI is coming, and if it's not Clona, it's somebody else," Reid told 404 Media.

Prototype learning, a popular machine learning method designed for inherently interpretable decisions, leverages similarities to learned prototypes for classifying new data. While it is mainly applied in computer vision, in this work, we build upon prior research and further explore the extension of prototypical networks to natural language processing. We introduce a learned weighted similarity measure that enhances the similarity computation by focusing on informative dimensions of pre-trained sentence embeddings. Additionally, we propose a post-hoc explainability mechanism that extracts prediction-relevant words from both the prototype and input sentences. Finally, we empirically demonstrate that our proposed method not only improves predictive performance on the AG News and RT Polarity datasets over a previous prototype-based approach, but also improves the faithfulness of explanations compared to rationale-based recurrent convolutions.

Ill-posed linear inverse problems arise frequently in various applications, from computational photography to medical imaging. A recent line of research exploits Bayesian inference with informative priors to handle the ill-posedness of such problems. Amongst such priors, score-based generative models (SGM) have recently been successfully applied to several different inverse problems. In this study, we exploit the particular structure of the prior defined by the SGM to define a sequence of intermediate linear inverse problems. As the noise level decreases, the posteriors of these inverse problems get closer to the target posterior of the original inverse problem. To sample from this sequence of posteriors, we propose the use of Sequential Monte Carlo (SMC) methods. The proposed algorithm, MCGDiff, is shown to be theoretically grounded and we provide numerical simulations showing that it outperforms competing baselines when dealing with ill-posed inverse problems in a Bayesian setting.

The large language based-model chatbot ChatGPT gained a lot of popularity since its launch and has been used in a wide range of situations. This research centers around a particular situation, when the ChatGPT is used to produce news that will be consumed by the population, causing the facilitation in the production of fake news, spread of misinformation and lack of trust in news sources. Aware of these problems, this research aims to build an artificial intelligence model capable of performing authorship attribution on news articles, identifying the ones written by the ChatGPT. To achieve this goal, a dataset containing equal amounts of human and ChatGPT written news was assembled and different natural processing language techniques were used to extract features from it that were used to train, validate and test three models built with different techniques. The best performance was produced by the Bidirectional Long Short Term Memory (LSTM) Neural Network model, achiving 91.57\% accuracy when tested against the data from the testing set.

In recent years, we witness the explosion of false and unconfirmed information (i.e., rumors) that went viral on social media and shocked the public. Rumors can trigger versatile, mostly controversial stance expressions among social media users. Rumor verification and stance detection are different yet relevant tasks. Fake news debunking primarily focuses on determining the truthfulness of news articles, which oversimplifies the issue as fake news often combines elements of both truth and falsehood. Thus, it becomes crucial to identify specific instances of misinformation within the articles. In this research, we investigate a novel task in the field of fake news debunking, which involves detecting sentence-level misinformation. One of the major challenges in this task is the absence of a training dataset with sentence-level annotations regarding veracity. Inspired by the Multiple Instance Learning (MIL) approach, we propose a model called Weakly Supervised Detection of Misinforming Sentences (WSDMS). This model only requires bag-level labels for training but is capable of inferring both sentence-level misinformation and article-level veracity, aided by relevant social media conversations that are attentively contextualized with news sentences. We evaluate WSDMS on three real-world benchmarks and demonstrate that it outperforms existing state-of-the-art baselines in debunking fake news at both the sentence and article levels.

Radio Frequency Fingerprinting (RFF) techniques promise to authenticate wireless devices at the physical layer based on inherent hardware imperfections introduced during manufacturing. Such RF transmitter imperfections are reflected into over-the-air signals, allowing receivers to accurately identify the RF transmitting source. Recent advances in Machine Learning, particularly in Deep Learning (DL), have improved the ability of RFF systems to extract and learn complex features that make up the device-specific fingerprint. However, integrating DL techniques with RFF and operating the system in real-world scenarios presents numerous challenges. This article identifies and analyzes these challenges while considering the three reference phases of any DL-based RFF system: (i) data collection and preprocessing, (ii) training, and finally, (iii) deployment. Our investigation points out the current open problems that prevent real deployment of RFF while discussing promising future directions, thus paving the way for further research in the area.

Kurt "The CyberGuy" Knutsson introduces Somatic's AI janitor robot that was created to help with cleaning restrooms. Oregon State University is warning students to "avoid all robots" and to "not open" any food delivery robots due to an ongoing bomb threat on the campus. On Tuesday afternoon, Oregon State University (OSU) issued an alert to students at the Corvallis, Oregon, university that there was a bomb threat related to the Starship food delivery robots. Oregon State University told students to avoid Starship food delivery robots due to a bomb threat. OSU advised people not open the robots and to avoid them "until further notice."

FOX News contributor Mollie Hemingway and Rebelle Communications founder and CEO Laura Fink discuss the U.S. media reporting that Israel was responsible for killing 500 people in a Gaza hospital bombing on'MediaBuzz.' JERUSALEM – Over the past two weeks, since Palestinian terrorist group Hamas carried out its deadly attack in southern Israel killing some 1,400 Israelis, there is a fear that a new front in the old war between Israelis and Palestinians could open up – in the digital realm. While doctored images and fake news have long been part of the Middle East wartime arsenal, with the arrival less than a year ago of easy-to-use artificial intelligence (AI) generative tools it seems highly probable that deepfake visuals will soon be making an appearance on the war front too. "Hamas and other Palestinian factions have already passed off gruesome images from other conflicts as though they were Palestinian victims of Israeli assaults, so this is not something unique to this theater of operations," David May, a research manager at the Foundation for Defense of Democracies, told Fox News Digital. He described how in the past, Hamas has been known to intimidate journalists into not reporting about its use of human shields in the Palestinian enclave, as well as staging images of toddlers and teddy bears buried in the rubble. Hamas killed at least 1,400 in a surprise terror attack that hit men, women, children and older civilians on Oct. 7. (Getty) "Hamas controls the narrative in the Gaza Strip," said May, who follows Hamas' activities closely, adding that "AI-generated images will complicate an Israeli-Palestinian conflict already rife with disinformation."