AI-generated content is rapidly becoming a salient component of online information ecosystems, yet its influence on public trust and epistemic judgments remains poorly understood. We present a large-scale mixed-design experiment (N = 1,000) investigating how AI-generated credibility scores affect user perception of political news. Our results reveal that AI feedback significantly moderates partisan bias and institutional distrust, surpassing traditional engagement signals such as likes and shares. These findings demonstrate the persuasive power of generative AI and suggest a need for design strategies that balance epistemic influence with user autonomy.

Although the terms are often confused or used interchangeably, sperm and semen are not the same thing. Semen is the fluid that comes out of the penis, while sperm are the microscopic cells within the semen. Sperm cells are specialized for the task of fertilizing an egg. Semen analysis is considered essential for diagnosis of male infertility, but is not readily available at medical institutions other than those specializing in infertility treatment. 'Fertility specialists take it for granted that the first step in diagnosing male infertility is to perform a semen analysis,' Professor Kobayashi added.

Abstract--Music genre classification is an area that utilizes machine learning models and techniques for the processing of audio signals, in which applications range from content recommendation systems to music recommendation systems. In this research I explore various machine learning algorithms for the purpose of music genre classification, using features extracted from audio signals.The systems are namely, a Multilayer Perceptron (built from scratch), a k-Nearest Neighbours (also built from scratch), a Convolutional Neural Network and lastly a Random Forest wide model. In order to process the audio signals, feature extraction methods such as Short-Time Fourier Transform, and the extraction of Mel Cepstral Coefficients (MFCCs), is performed. Through this extensive research, I aim to asses the robustness of machine learning models for genre classification, and to compare their results. Music is a form of expression, a universal language that is easy to translate into cultural stories and different emotions.

Transcription service companies continue to make claims about their AI, but in the absence of any formal set of benchmarks for natural language processing (NLP), most of those claims lack the context required to make a precise apples-to-apples comparison. Dialpad, for example, recently announced that its Voice Intelligence AI technology has surpassed rivals in terms of both keyword and general accuracy. Along with Google, IBM, Microsoft, Cisco, Chorus.ai, Avaya, Zoom, and Amazon Web Services (AWS), Dialpad provides end users with automated speech recognition (ASR) and NLP capabilities that can be applied to voice calls and videoconferencing sessions in real time. An analysis of general word and keyword accuracy Dialpad published claims it achieves a word accuracy rating of 82.3% and a keyword accuracy rating of 92.5%.

It's hard to ignore the ways artificial intelligence (AI) has already bested humans, from making incredibly convincing internet videos to beating humanity's best Go players. AI is expected to outpace humans in a number of occupations; some of which might be a little unexpected. A new study released this week from LawGeex, a leading AI contract review platform, has revealed a new area in which AI outperforms us: Law. For the study, 20 human attorneys were pitted against LawGeex's AI in reviewing 5 NDAs. The controlled conditions of the study were designed to resemble how lawyers would typically review and approve everyday contracts.

On Dec. 14, China's Ministry of Industry and Information Technology (MIIT) published its plan to develop China's artificial intelligence industry over the next three years. The MIIT report declares China's intention to gain an international competitive advantage in AI by 2020. What's in the report: The MIIT report identifies eight key AI development goals. The systems should also be able to support face recognition of people from different ethnicities. The devices should also detect user intent with an accuracy rating of over 90 percent.