AI developments from generating videos, voices, pictures and human-like conversations are growing rapidly, lawmakers say they are trying to keep up. WASHINGTON, D.C. – As the ramifications of AI's growing ability become increasingly apparent, lawmakers on Capitol Hill graded their understanding of the new technology from one to 10. "I put my knowledge on a scale of one to 10 at about a 1.5," Republican Sen. Cynthia Lummis said, giving herself the lowest rating among the politicians who spoke with Fox News. Another Republican, Rep. Nancy Mace of South Carolina, had the most confident response. "I'd say it's eight or nine," she said. Elon Musk and more than 1,000 others in March called for an immediate pause on "giant AI experiments," warning the rapidly developing sector may pose security threats.

'DEEPLY CONCERNING' - Republicans respond after IRS whistleblower says Hunter Biden investigation is being mishandled. 'FRIGHTENING' - Fetterman's opening statement upon return to Senate after hospitalization raises eyebrows. WATCH THE WALLET - Crypto criminals beware: AI is after you. MISINFORMATION MACHINES – AI chatbot'hallucinations' could pose political, intellectual, institutional dangers. Continue reading … ROYAL REACTION - Lee Cohen explains why Meghan Markle deserves praise for skipping the coronation,.

Fake AI pictures and videos will be nearly impossible to discern from real images as the technology behind deepfakes advances, University of California, Berkeley professor says. AI-generated pictures, videos and voices -- called deepfakes -- are so believable and widely available that people will soon not be able to discern between real and manipulated media, an image analyst told Fox News. "What's important about deepfakes is not, 'oh, we can manipulate audio, images and videos'--we've always been able to do that," said Hany Farid, a professor at University of California, Berkeley's School of Information. "But we've democratized access to technology that used to be in the hands of the few, and now are in the hands of the many." "When we enter this world where any audio, image or video can be manipulated, well, then how do you believe anything?"

Artificial intelligence "hallucinations" — misinformation created both accidentally and intentionally — will challenge the trustworthiness of many institutions, experts say.

Former Major Label Music executive Seth Schachner explains how the introduction of A.I. could have devastating effects on the music industry on'Fox News @ Night.' Replika CEO Eugenia Kuyda, the creator of an AI dating app with millions of users around the world, spoke to Fox News Digital about AI companion bots and the future of human and AI relationships. It is an industry that she said will truly change people's lives. "I think it's the next big platform. I think it is going to be bigger than any other platform before that. I think it's going to be basically whatever the iPhone is for you right now." Kuyda said that the technology still needs time to improve, but she predicted that people around the world will have access to chatbots that accompany them on trips and are intimately aware of their lives within 5 to 10 years.

Artificial Intelligence poses both risks and rewards, but developers should be weary of technologies that could threaten "scary" outcomes, AI technologist says. Men who have trouble finding dates are reportedly turning to artificial intelligence and self-described love guru to craft appealing dating profiles. "My AI prompts and training can turn any guy from zero to hero," Stefan-Pierre Tomlin, a 32-year-old London model and self-described love guru, told South West News Service, according to the New York Post. Tomlin operates a website called Celebrity Love Coach where subscribers can pay between roughly $55 to $150 a month to receive his advice and "support to help you achieve your dating goals," according to the website. Subscribers also receive access to "bespoke" AI to draft appealing dating profiles.

Fox News contributor Joe Concha joins'Fox & Friends First' to discuss Elon Musk's warning that artificial intelligence could threaten elections and his concerns on the declining birth rate. A government watchdog group Tuesday requested that Missouri Attorney General Andrew Bailey investigate a tax-payer-funded Missouri State University business boot program that excluded White males. In a letter to Bailey's office, the Equal Protection Project (EPP) alleged that Missouri State University (MSU) was "engaging in racial- and gender-based discrimination through its sponsorship, promotion, and hosting of a small business training'boot camp' that limits participation" to women and people who identify as "BIPOC" – an acronym for non-white "Black, Indigenous and Persons of Color." MSU began accepting applications for the Spring 2023 Early-Stage Business Boot Camp program in late November. The university said the program was for "aspiring or current BIPOC and/or women small business owners who have recently started or are in the idea phase" and live in Southern Missouri.

People form judgments and make decisions based on the information that they observe. A growing portion of that information is not only provided, but carefully curated by social media platforms. Although lawmakers largely agree that platforms should not operate without any oversight, there is little consensus on how to regulate social media. There is consensus, however, that creating a strict, global standard of "acceptable" content is untenable (e.g., in the US, it is incompatible with Section 230 of the Communications Decency Act and the First Amendment). In this work, we propose that algorithmic filtering should be regulated with respect to a flexible, user-driven baseline. We provide a concrete framework for regulating and auditing a social media platform according to such a baseline. In particular, we introduce the notion of a baseline feed: the content that a user would see without filtering (e.g., on Twitter, this could be the chronological timeline). We require that the feeds a platform filters contain "similar" informational content as their respective baseline feeds, and we design a principled way to measure similarity. This approach is motivated by related suggestions that regulations should increase user agency. We present an auditing procedure that checks whether a platform honors this requirement. Notably, the audit needs only black-box access to a platform's filtering algorithm, and it does not access or infer private user information. We provide theoretical guarantees on the strength of the audit. We further show that requiring closeness between filtered and baseline feeds does not impose a large performance cost, nor does it create echo chambers.

This paper examines the diffusion of media slant, specifically how partisan content from national cable news affects local newspapers in the U.S., 2005-2008. We use a text-based measure of cable news slant trained on content from Fox News Channel (FNC), CNN, and MSNBC to analyze how local newspapers adopt FNC's slant over CNN/MSNBC's. Our findings show that local news becomes more similar to FNC content in response to an exogenous increase in local FNC viewership. This shift is not limited to borrowing from cable news, but rather, local newspapers' own content changes. Further, cable TV slant polarizes local news content.

Harmful content is pervasive on social media, poisoning online communities and negatively impacting participation. A common approach to address this issue is to develop detection models that rely on human annotations. However, the tasks required to build such models expose annotators to harmful and offensive content and may require significant time and cost to complete. Generative AI models have the potential to understand and detect harmful content. To investigate this potential, we used ChatGPT and compared its performance with MTurker annotations for three frequently discussed concepts related to harmful content: Hateful, Offensive, and Toxic (HOT). We designed five prompts to interact with ChatGPT and conducted four experiments eliciting HOT classifications. Our results show that ChatGPT can achieve an accuracy of approximately 80% when compared to MTurker annotations. Specifically, the model displays a more consistent classification for non-HOT comments than HOT comments compared to human annotations. Our findings also suggest that ChatGPT classifications align with provided HOT definitions, but ChatGPT classifies "hateful" and "offensive" as subsets of "toxic." Moreover, the choice of prompts used to interact with ChatGPT impacts its performance. Based on these in-sights, our study provides several meaningful implications for employing ChatGPT to detect HOT content, particularly regarding the reliability and consistency of its performance, its understand-ing and reasoning of the HOT concept, and the impact of prompts on its performance. Overall, our study provides guidance about the potential of using generative AI models to moderate large volumes of user-generated content on social media.