Speaking with popular AI content creators convinces me that "slop" isn't just the internet rotting in real time, but the early draft of a new kind of pop culture. Lately, everywhere I scroll, I keep seeing the same fish-eyed CCTV view: a grainy wide shot from the corner of a living room, a driveway at night, an empty grocery store. JD Vance shows up at the doorstep in a crazy outfit. A car folds into itself like paper and drives away. A cat comes in and starts hanging out with capybaras and bears, as if in some weird modern fairy tale. This fake-surveillance look has become one of the signature flavors of what people now call AI slop. For those of us who spend time online watching short videos, slop feels inescapable: a flood of repetitive, often nonsensical AI-generated clips that washes across TikTok, Instagram, and beyond. For that, you can thank new tools like OpenAI's Sora (which exploded in popularity after launching in app form in September), Google's Veo series, and AI models built by Runway. Now anyone can make videos, with just a few taps on a screen.

The persistent challenge of bias in machine learning models necessitates robust solutions to ensure parity and equal treatment across diverse groups, particularly in classification tasks. To address this, we propose a novel methodology grounded in bilevel optimization principles. Our deep learning-based approach concurrently optimizes for both accuracy and fairness objectives, and under certain assumptions, achieving proven Pareto optimal solutions while mitigating bias in the trained model. Theoretical analysis indicates that the upper bound on the loss incurred by this method is less than or equal to the loss of the Lagrangian approach, which involves adding a regularization term to the loss function. We demonstrate the efficacy of our model primarily on tabular datasets such as UCI Adult and Heritage Health.

To enforce a strict licensing model, the UK would also need to restrict access to models that have been trained on such content, which could include US-owned AI systems. With the Trump administration signalling it will not pursue strict AI regulations and China pursuing AI growth at "breakneck speed", the UK could weaken its economic and national security interests by lagging in the AI race, said TBI. "If the UK imposes laws that are too strict, it risks falling behind in the AI-driven economy and weakening its capacity to protect national security interests," said TBI. The report said arguing that commercial AI models cannot be trained on content from the open web was close to saying knowledge workers – a broad category of professionals ranging from lawyers to researchers – cannot profit from insights they get when reading the same content. Rather than fighting to uphold outdated regulations, said TBI, rights holders and policymakers should help build a future where creativity is valued alongside AI innovation. Fernando Garibay, a record producer who has worked with artists including Lady Gaga and U2, said history has been dotted with "end-of-time claims" related to technological breakthroughs, from the printing press to music streaming.

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and act like humans. AI technologies enable computers to perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. A study led by a professor from the University of Central Florida has identified six challenges that must be overcome in order to improve our relationship with artificial intelligence (AI) and guarantee its ethical and fair utilization. A professor from the University of Central Florida and 26 other scientists have published a study highlighting the obstacles that humanity must tackle to guarantee that artificial intelligence (AI) is dependable, secure, trustworthy, and aligned with human values. The study was published in the International Journal of Human-Computer Interaction.

Social media has become a dominant form of communication for individuals, and for companies looking to market and sell their products and services. Properly understanding and responding to customer feedback on Twitter, Facebook and other social media platforms is critical for success, but it is incredibly labor intensive. That's where sentiment analysis comes in. The term refers to the automated process of identifying the emotion -- either positive, negative or neutral -- associated with text. While artificial intelligence refers to logical data analysis and response, sentiment analysis is akin to correctly identifying emotional communication.

A new AI tool funded in part by the U.S. military has proven adept at a task that has traditionally been very difficult for computer programs: detecting the human art of sarcasm. It could help intelligence officers or agencies better apply artificial intelligence to trend analysis by avoiding social media posts that aren't serious. Certain words in specific combinations can be a predictable indicator of sarcasm in a social media post, even if there isn't much other context, two researchers from the University of Central Florida noted in a March paper in the journal Entropy. Using a variety of datasets of posts from Twitter, Reddit, various dialogues and even headlines from The Onion, Garibay and his colleague Ramya Akula mapped out how some key words relate to other words. "For instance, words such as'just', 'again', 'totally', '!', have darker edges connecting them with every other word in a sentence. These are the words in the sentence that hint at sarcasm and, as expected, these receive higher attention than others," they write.

No second thoughts about the fact that how critical has social media become a part of our lives. We rely on social media so much that today imagining a life without it does not sink in. No wonder social media is considered to be one of the best platforms to market and sell different products and services in addition to being a dominant form of communication. With this platform, you stand a chance to reach out to the maximum lot. While this medium is used for driving sales, another area that it caters to is how are our customers reacting to what you are delivering.

Artificial Intelligence is one step closer to being more human-like as it can now detect sarcasm. Funded by the U.S military, a new AI tool has managed to do a task that is tough for computer algorithms to perform in general, identifying the tone and irony of the human voice. This advancement can help intelligence agencies perform better trend analysis by identifying social media posts that are basically sarcastic in nature and meaning no harm. How did the AI tool figure it out? According to two researchers from the University of Central Florida, some words in a set of combinations can be a clear indicator of sarcasm in social media posts.

Washington, May 11: Properly understanding and responding to customer feedback on social media platforms is crucial for brands, and it may have just gotten a little easier thanks to new research by computer science researchers at the University of Central Florida who have developed a sarcasm detector. Social media has become a dominant form of communication for individuals, and for companies looking to market and sell their products and services. Properly understanding and responding to customer feedback on Twitter, Facebook and other social media platforms are critical for success, but it is incredibly labour-intensive. Sarcastic Tweet Labelling Amsterdam Tulip Field As Kaas Pathaar Goes Viral; Netizens Come Up With Their Versions. That's where sentiment analysis comes in.

Computer science researchers at the University of Central Florida have developed a sarcasm detector. Social media has become a dominant form of communication for individuals, and for companies looking to market and sell their products and services. Properly understanding and responding to customer feedback on Twitter, Facebook and other social media platforms is critical for success, but it is incredibly labor intensive. That's where sentiment analysis comes in. The term refers to the automated process of identifying the emotion--either positive, negative or neutral--associated with text.