In November, the reality star and entrepreneur Kim Kardashian posted a series of images and videos to her social-media accounts, in which she appeared to promote Tesla's new A.I. robot, Optimus. In a video on X, captioned "Meet my new friend," Kardashian is seen engaging with Elon Musk's humanoid golem, which reportedly retails for around thirty thousand dollars, and whose metal torso is inscribed with the Tesla logo. "O.K., hi!" she says perkily, off camera, as she waves her manicured fingers just within frame--a motion that is immediately echoed by the robot. "Can you do this: 'I love you'?" she asks next, forming a half heart with her hand, proffering it to the robot to urge him to complete the shape, and gasping in awe as he eagerly complies. But Optimus, who in the video seems more than happy to be at his mistress's beck and call, appears less subservient in a series of pictures in which Kardashian, wearing spike heels and lingerie, poses beside him and a gold Tesla Cybercab.

The birth of ChatGPT brought a collection of anxieties regarding how large language models allow users to quickly subvert processes that once required human time, effort, passion, and understanding. And further, the tech sector's often stormy relationship with regulation and ethical oversight have left many fearful for a future where artificial intelligence replaces humans at work and stymies human creativity. While much of this alarm is well founded, we should also consider the possibility that human creativity can blossom in the age of AI. In 2025, we will start to see this manifest in our collective cultural response to technology. To examine how culture and creativity might adapt to the age of AI, we'll use hip-hop as an example.

This paper proposes a novel approach to develop an open-domain and long-form Over-The-Top (OTT) Question-Answering (QA) dataset, DragonVerseQA, specifically oriented to the fantasy universe of "House of the Dragon" and "Game Of Thrones" TV series. Most existing QA datasets focus on short, fact-based answers sourced almost solely from Wikipedia articles, devoid of depth and contextual richness for sophisticated narrative understanding. We curate a dataset that combines full episode summaries sourced from HBO and fandom wiki websites, user reviews from sources like IMDb and Rotten Tomatoes, and high-quality, open-domain, legally admissible sources, and structured data from repositories like WikiData into one dataset. The dataset provides a multi-dimensional context, reflecting complex character dynamics and plot developments from these varied sources. That means, on equal footing, only after heavy data preprocessing and filtering methods will meaningful, non-spam unbiased reviews be available in this enriched dataset. The comprehensive insights are given through the long-form answers generated from this enriched context. This is what makes this valuable dataset for improving conversational AI, narrative analysis, sentiment analysis, summarization techniques, and relation extraction. A comparative analysis with state-of-the-art QA datasets such as SQuAD 2.0, TriviaQA, and Natural Questions brings to light the unique advantages of our dataset in terms of contextual complexity and answer length. Detailed reviews add layers to audience sentiment and narrative interpretation, raising the bar for domain-specific QA with a new quality benchmark. Our work also allows a deeper understanding of entertainment-industry content and opens the door to more knowledgeable and creative AI-driven interactions within digital media environments.

The interplay between past, present, and future is a central theme in the iconic movie Back to the Future, where small alterations in past events have profound, cascading effects on the future [1]. This concept mirrors the intricate and often non-linear relationships in real-world systems, where predictions about the future are not merely passive observations but active drivers of current decisions and behaviors. In the film, the characters reshape their present and future by altering past events, reflecting the power of temporal causality--the idea that events in time are interconnected, and that actions taken now have consequences for the future. In much the same way, effective nowcasting--predicting short-term outcomes like weather, natural hazards, health events, or traffic patterns--should not only anticipate what will happen but also incorporate how those predictions can influence present decisions and conditions. Traditional nowcasting methods, however, often focus exclusively on making predictions about future states without considering the active feedback loop that can be created by those predictions [2].

As a crucial step to enhance LLMs alignment with human intentions, Instruction Fine-Tuning (IFT) has a high demand on dataset quality. However, existing IFT datasets often contain knowledge that is inconsistent with LLMs' internal knowledge learned from the pre-training phase, which can greatly affect the efficacy of IFT. To address this issue, we introduce NILE (iNternal consIstency aLignmEnt) framework, aimed at optimizing IFT datasets to unlock LLMs' capability further. NILE operates by eliciting target pre-trained LLM's internal knowledge corresponding to instruction data. The internal knowledge is leveraged to revise the answer in IFT datasets. Additionally, we propose a novel Internal Consistency Filtering (ICF) method to filter training samples, ensuring its high consistency with LLM's internal knowledge. Our experiments demonstrate that NILE-aligned IFT datasets sharply boost LLM performance across multiple LLM ability evaluation datasets, achieving up to 66.6% gain on Arena-Hard and 68.5% on Alpaca-Eval V2. Further analysis confirms that each component of the NILE}framework contributes to these substantial performance improvements, and provides compelling evidence that dataset consistency with pre-trained internal knowledge is pivotal for maximizing LLM potential.

The courageous story of Afghanistan's first all-girls robotics team is coming to a theater near you. Rule Breakers is based on the true story of The Afghan Girls Robotics Team, who grabbed the world's attention when they were denied member visas by the United States in 2017 while attempting to compete at the First Global Challenge international robotics competition. Fifty three members of Congress signed a petition and President Donald Trump intervened to give the girls travel documents on special humanitarian grounds allowing them to enter the US and compete in the robotics games, according to a New York Times profile. The story of the team's struggle to compete in the robotics competition goes much deeper than their attempts to enter the US. First Global founder Dean Kamen, who is best known for designing the Segway, put together his competitive robotics league as a way to spark interest in science and technology among high schoolers.

For more than a decade, between the late 80s and the dawn of the 21st century, Sega was one of the coolest video game companies on the planet. Its arcade games, from Golden Axe to Virtua Fighter, were blockbuster successes; the Mega Drive brought a punk rock attitude to the home console scene, challenging Nintendo's family friendly approach with eye-pummelling TV commercials and censor-baiting games such as Mortal Kombat and Night Trap. Arguably though, it was later, in the Dreamcast era, that Sega's studios were producing their most innovative and extravagant work. The likes of Jet Set Radio, Crazy Taxi and Space Channel 5 were hypercolourful celebrations of Tokyo pop culture. Now, the man who managed Sega Japan's developers at that time, Shuji Utsumi, is the CEO of Sega America and Europe – and he has a plan to restore the company to its creative heights.

Artificial intelligence is everywhere you look right now, making its way into music streaming, social media, video games, web search, and just about every other technological field. Every time a new phone or laptop is launched these days, what's invariably mentioned first is just how much AI it has on board. AI's reach also extends deeply into mobile photography. It started with the smart, algorithm-led tweaks to color and brightness in your mobile photos. Both Android and iOS also apply machine algorithms to make colors in photos "pop" and to add more dynamics to images.

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Music Genre Classification is one of the most popular topics in the fields of Music Information Retrieval (MIR) and digital signal processing. Deep Learning has emerged as the top performer for classifying music genres among various methods. The letter introduces a novel approach by combining ensemble learning with attention to sub-components, aiming to enhance the accuracy of identifying music genres. The core innovation of our work is the proposal to classify the subcomponents of the music pieces separately, allowing our model to capture distinct characteristics from those sub components. By applying ensemble learning techniques to these individual classifications, we make the final classification decision on the genre of the music. The proposed method has superior advantages in terms of accuracy compared to the other state-of-the-art techniques trained and tested on the GTZAN dataset.