Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Tech giant Baidu on Wednesday received approval by Chinese authorities to launch its artificial intelligence Ernie Bot to the general public starting Aug. 31, a spokesperson told Reuters. Baidu became the first company to receive such approval after regulatory setbacks and is also set to launch a suite of new AI-native apps. The company has been embedding Ernie, which resembles OpenAI's ChatGPT, into its search engine and other products, allowing many of them to gain market share while waiting for Chinese regulators' approval.

A faux-rap song that features the AI-generated voice of Donald Trump defending himself against the criminal indictment out of Georgia has jumped to the No. 2 spot on the iTunes rap chart. Hi-Rez the Rapper, who describes himself as a radical freedom extremist, released the song on August 25, one day after Trump posted his August 24th mugshot to X (formerly Twitter). The song has been viewed nearly 3million times on X, and has shot to the top of the Hip-Hop charts since its release. The digital imitation of the former president's voice is nearly identical to the real-life 2024 candidate's. In the song, Hi-Rez imagines how Trump responded to being booked and released on bond on charges of attempting to overturn the 2020 election in Georgia.

Stay up to date on the latest AI technology advancements and learn about the challenges and opportunities AI presents now and for the future.

In the past few years, multiple researchers claim to have shown that large language models can pass cognitive tests designed for humans, from working through problems step by step, to guessing what other people are thinking. These kinds of results are feeding a hype machine predicting that these machines will soon come for white-collar jobs; that they could replace teachers, doctors, journalists, and lawyers. Geoffrey Hinton has called out GPT-4's apparent ability to string together thoughts as one reason he is now scared of the technology he helped create. There's little agreement on what those results really mean. Some people are dazzled by what they see as glimmers of human-like intelligence, while others aren't convinced one bit.

"Jump the fuck up!" Tom Morello, the guitarist for Rage Against the Machine, instructed the crowd outside the gates of Paramount. Morello, who wore his signature red bandana around his neck, was strumming "This Land Is Your Land," to rev up the morning's picketers. Everyone raised a fist and jumped the fuck up, singing, "This land was made for you and me!" The Writers Guild of America was on day one hundred and three of its strike against the Hollywood studios, represented by the Alliance of Motion Picture and Television Producers (A.M.P.T.P.); the actors of SAG-AFTRA were on day thirty. The August sun was blazing, and the experienced strikers wore hats; others found shade under signs that read "ON STRIKE!" or "CUT OUT THE CRAP AMPTP!" It was "Bruce Springsteen Day" on the Paramount line, and several people had come in "Born in the U.S.A." garb. A guy in a headband and tight jeans marched along Melrose Avenue. "I'm here so often I plan my outfits," he said to a companion.

Since their inception in 2014, Generative Adversarial Networks (GANs) have rapidly emerged as powerful tools for generating realistic and diverse data across various domains, including computer vision and other applied areas. Consisting of a discriminative network and a generative network engaged in a Minimax game, GANs have revolutionized the field of generative modeling. In February 2018, GAN secured the leading spot on the ``Top Ten Global Breakthrough Technologies List'' issued by the Massachusetts Science and Technology Review. Over the years, numerous advancements have been proposed, leading to a rich array of GAN variants, such as conditional GAN, Wasserstein GAN, CycleGAN, and StyleGAN, among many others. This survey aims to provide a general overview of GANs, summarizing the latent architecture, validation metrics, and application areas of the most widely recognized variants. We also delve into recent theoretical developments, exploring the profound connection between the adversarial principle underlying GAN and Jensen-Shannon divergence, while discussing the optimality characteristics of the GAN framework. The efficiency of GAN variants and their model architectures will be evaluated along with training obstacles as well as training solutions. In addition, a detailed discussion will be provided, examining the integration of GANs with newly developed deep learning frameworks such as Transformers, Physics-Informed Neural Networks, Large Language models, and Diffusion models. Finally, we reveal several issues as well as future research outlines in this field.

Abstract--Emotion recognition using electroencephalogram (EEG) mainly has two scenarios: classification of the discrete labels and regression of the continuously tagged labels. Although many algorithms were proposed for classification tasks, there are only a few methods for regression tasks. For emotion regression, the label is continuous in time. A natural method is to learn the temporal dynamic patterns. In previous studies, long short-term memory (LSTM) and temporal convolutional neural networks (TCN) were utilized to learn the temporal contextual information from feature vectors of EEG. However, the spatial patterns of EEG were not effectively extracted. To enable the spatial learning ability of TCN towards better regression and classification performances, we propose a novel unified model, named MASA-TCN, for EEG emotion regression and classification tasks. The space-aware temporal layer enables TCN to additionally learn from spatial relations among EEG electrodes. Besides, a novel multi-anchor block with attentive fusion is proposed to learn dynamic temporal dependencies. Experiments on two publicly available datasets show MASA-TCN achieves higher results than the state-of-the-art methods for both EEG emotion regression and classification tasks.

One of the biggest challenges of natural language generation (NLG) is the proper handling of named entities. Named entities are a common source of grammar mistakes such as wrong prepositions, wrong article handling, or incorrect entity inflection. Without factoring linguistic representation, such errors are often underrepresented when evaluating on a small set of arbitrarily picked argument values, or when translating a dataset from a linguistically simpler language, like English, to a linguistically complex language, like Russian. However, for some applications, broadly precise grammatical correctness is critical -- native speakers may find entity-related grammar errors silly, jarring, or even offensive. To enable the creation of more linguistically diverse NLG datasets, we release a Corpus of Linguistically Significant Entities (CLSE) annotated by linguist experts. The corpus includes 34 languages and covers 74 different semantic types to support various applications from airline ticketing to video games. To demonstrate one possible use of CLSE, we produce an augmented version of the Schema-Guided Dialog Dataset, SGD-CLSE. Using the CLSE's entities and a small number of human translations, we create a linguistically representative NLG evaluation benchmark in three languages: French (high-resource), Marathi (low-resource), and Russian (highly inflected language). We establish quality baselines for neural, template-based, and hybrid NLG systems and discuss the strengths and weaknesses of each approach.

A prevalent practice in recommender systems consists in averaging item embeddings to represent users or higher-level concepts in the same embedding space. This paper investigates the relevance of such a practice. For this purpose, we propose an expected precision score, designed to measure the consistency of an average embedding relative to the items used for its construction. We subsequently analyze the mathematical expression of this score in a theoretical setting with specific assumptions, as well as its empirical behavior on real-world data from music streaming services. Our results emphasize that real-world averages are less consistent for recommendation, which paves the way for future research to better align real-world embeddings with assumptions from our theoretical setting.

A bipartisan panel of voters weighed in on the future of artificial intelligence and growing concerns surrounding the potential dangers of the emerging technology. A U.K.-based tech expert said he is not losing sleep at night over the recent growth of artificial intelligence but argued he does have concerns over AI potentially becoming a hellish boss that oversees an employee's every move. Michael Wooldridge is a professor of computer science at the University of Oxford who has been a leading expert on AI for at least 30 years. He spoke with The Guardian this month regarding upcoming lectures he will lead this winter to demystify artificial intelligence, while noting what concerns he does have with the tech. He told the outlet that he does not share the same worries as some AI experts who warn the powerful systems could one day lead to the downfall of humanity.