Fox News medical contributor Dr. Marty Makary joined'America's Newsroom' to discuss the breakthrough and what it means for artificial intelligence as it is applied to medicine. Rock star Roger Waters of Pink Floyd has a history of controversial public displays, but the Campaign Against Antisemitism has released a documentary accusing him of antisemitism directly. Waters has repeatedly been criticized for shocking imagery at his concerts. Earlier this year he was condemned for a concert in Germany where he wore what appeared to be a Nazi-inspired uniform. Screens at the same concert listed the names of victims presumed to have been killed by state actors, including George Floyd and anti-Nazi activist Sophie Scholl.

Feel like your smart home assistant needs a bit of an upgrade? Kurt "The CyberGuy" Knutsson shares steps on how you can change your Amazon Alexa's name and accent. Ever felt that your smart home assistant's voice doesn't quite match your aesthetic or mood? Well, Alexa is lending an ear to your preferences. There's a charm in personalizing every tiny detail of our gadgets, and Amazon's Alexa isn't one to be left behind.

Fox News Flash top entertainment and celebrity headlines are here. Sarah Ferguson expressed her shock and grief as she mourned the death of her former personal assistant, Jenean Chapman, who was murdered in Texas this week. The 63-year-old Duchess of York paid tribute to Chapman in an Instagram post that she shared on Thursday. "I am shocked and saddened to learn that Jenean Chapman, who worked with me as my personal assistant many years ago, has been murdered in Dallas aged just 46. A suspect is in custody," Ferguson wrote.

There's a bill in US Congress right now to stop AI from gaining control of nuclear weapons, and roughly a dozen militaries around the world are investigating the possibilities of autonomous weaponry. That's why watching The Creator, a movie set roughly 40 years from now, feels surreal, jarring, and oddly welcome. From Metropolis to Terminator, sci-fi has taught us to fear the AI revolt. This one opts to wonder what would happen if AI got so empathetic to humanity it wanted to save people from themselves. In writer-director Gareth Edwards' latest, war has laid waste to both humans and robots.

Authorship identification ascertains the authorship of texts whose origins remain undisclosed. That authorship identification techniques work as reliably as they do has been attributed to the fact that authorial style is properly captured and represented. Although modern authorship identification methods have evolved significantly over the years and have proven effective in distinguishing authorial styles, the generalization of stylistic features across domains has not been systematically reviewed. The presented work addresses the challenge of enhancing the generalization of stylistic representations in authorship identification, particularly when there are discrepancies between training and testing samples. A comprehensive review of empirical studies was conducted, focusing on various stylistic features and their effectiveness in representing an author's style. The influencing factors such as topic, genre, and register on writing style were also explored, along with strategies to mitigate their impact. While some stylistic features, like character n-grams and function words, have proven to be robust and discriminative, others, such as content words, can introduce biases and hinder cross-domain generalization. Representations learned using deep learning models, especially those incorporating character n-grams and syntactic information, show promise in enhancing representation generalization. The findings underscore the importance of selecting appropriate stylistic features for authorship identification, especially in cross-domain scenarios. The recognition of the strengths and weaknesses of various linguistic features paves the way for more accurate authorship identification in diverse contexts.

While large pre-trained language models (LLMs) have shown their impressive capabilities in various NLP tasks, they are still under-explored in the misinformation domain. In this paper, we examine LLMs with in-context learning (ICL) for news claim verification, and find that only with 4-shot demonstration examples, the performance of several prompting methods can be comparable with previous supervised models. To further boost performance, we introduce a Hierarchical Step-by-Step (HiSS) prompting method which directs LLMs to separate a claim into several subclaims and then verify each of them via multiple questions-answering steps progressively. Experiment results on two public misinformation datasets show that HiSS prompting outperforms state-of-the-art fully-supervised approach and strong few-shot ICL-enabled baselines.

We present a method for zero-shot recommendation of multimodal non-stationary content that leverages recent advancements in the field of generative AI. We propose rendering inputs of different modalities as textual descriptions and to utilize pre-trained LLMs to obtain their numerical representations by computing semantic embeddings. Once unified representations of all content items are obtained, the recommendation can be performed by computing an appropriate similarity metric between them without any additional learning. We demonstrate our approach on a synthetic multimodal nudging environment, where the inputs consist of tabular, textual, and visual data.

Recent developments in large language models (LLMs) have been impressive. However, these models sometimes show inconsistencies and problematic behavior, such as hallucinating facts, generating flawed code, or creating offensive and toxic content. Unlike these models, humans typically utilize external tools to cross-check and refine their initial content, like using a search engine for fact-checking, or a code interpreter for debugging. Inspired by this observation, we introduce a framework called CRITIC that allows LLMs, which are essentially "black boxes" to validate and progressively amend their own outputs in a manner similar to human interaction with tools. More specifically, starting with an initial output, CRITIC interacts with appropriate tools to evaluate certain aspects of the text, and then revises the output based on the feedback obtained during this validation process. Comprehensive evaluations involving free-form question answering, mathematical program synthesis, and toxicity reduction demonstrate that CRITIC consistently enhances the performance of LLMs. Meanwhile, our research highlights the crucial importance of external feedback in promoting the ongoing self-improvement of LLMs.

Imagine walking into work, feeling the hum of the office around you and settling into your desk, all the while unaware of the unseen eyes monitoring your every move. It's happening on a grand scale as corporations use advanced software to keep tabs on their employees. Experts fear that this extensive data collection could be a stepping stone, a way to train AI to replace human roles in the workforce. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK TIPS, TECH REVIEWS AND EASY HOW-TO'S TO MAKE YOU SMARTER It's a familiar story – the experienced worker trains the newbie, only to be replaced once the newcomer is up to speed. This age-old tale is on the verge of adding a new character to its plot: artificial intelligence.

For years now, Hakwan Lau has suffered from an inner torment. Lau is a neuroscientist who studies the sense of awareness that all of us experience during our every waking moment. How this awareness arises from ordinary matter is an ancient mystery. Several scientific theories purport to explain it, and Lau feels that one of them, called integrated information theory (IIT), has received a disproportionate amount of media attention. He's annoyed that its proponents tout it as the dominant theory in the press.