Mirror Me's Black Panther ran about 100 meters in under 10 seconds. A Chinese team has unveiled a groundbreaking quadruped robot that is pushing the boundaries of robotics and speed. The Black Panther 2.0, developed by Zhejiang University's humanoid innovation institute in collaboration with the Hangzhou-based startup Mirror Me, has achieved a remarkable feat by running approximately 100 meters in under 10 seconds. The design of the Black Panther 2.0 draws inspiration from various animals, resulting in a highly efficient biomechanical structure. Its carbon-fiber shins are modeled after jerboa desert rodents, increasing stiffness by an impressive 135% while only adding 16% to its weight.

Rep. Brandon Gill, R- Tex., got into a heated exchange with CNN host Pamela Brown over the Trump administration's crackdown on government spending, specifically for public broadcasting at PBS and NPR. By 1970, both PBS and NPR sprang forth from the CBP, and Americans were treated to the "News Hour," "Sesame Street," British comedies and science programming at a time when there were only three networks, cable TV was strictly for the boondocks, and VCRs were science fiction. A big part of the reason that programming was limited was that production costs for broadcasting were incredibly high. In David Grzybowski's book, 'The Big Story,' he cites Philadelphia news anchor Larry Kane talking about how hard it was during the 1979 Three Mile Island nuclear scare to just get a live TV shot from Harrisburg to Philly: "I know we had a live microwave, but the microwaves didn't go that far. I think we sought some satellite time. The satellite times in those days were 5,000 a minute."

OnePlus has been a bit up and down since it merged with Oppo back in 2021. It gained greater access to powerful components and partnerships with brands like Hasselblad, while its software and product lineup took a few steps back before finding its stride again. But now, three generations after the merger, OnePlus' latest flagship phone -- the OnePlus 13 -- feels like a fantastic return to form. In some areas, the company is even pushing the limits of hardware and gadget design in ways that rivals from Samsung and Google aren't. And with a starting price of 900, OnePlus has managed to undercut its closest competitor too, which makes this phone a great choice for anyone who cares more about getting hardware upgrades than fancy new AI tricks.

Sylvester Stallone is the latest celebrity embracing artificial intelligence. The "Rocky" star invested, along with several others, in Largo.ai, an AI-driven analytics platform for film, TV and advertising, raising 7.5 million in financing for the company. "We are very happy to have a cinema legend like Sylvester Stallone supporting our journey. Stallone's story in cinema and the disruption that he created is truly very inspiring for any entrepreneur like me and his support in the new era of cinema with AI by being a partner in Largo.ai is truly encouraging for us," Largo.ai CEO and co-founder Sami Arpa told Fox News Digital in a statement.

Hasbro Entertainment and Legendary Entertainment have joined forces to bring Magic: The Gathering to the big and small screens. The pair have signed a licensing deal to create "a live-action feature film and television universe" inspired by the card game. First up will be a movie, with other media to follow, but that's all that's been revealed so far. Longtime MTG fans might feel skeptical about this announcement, because this isn't the first time the intellectual property has been promised some kind of film or television adaptation. The card game's Fandom wiki page lists many of the proposed movie projects over the years.

Retrieval-augmented generation (RAG) has emerged as a promising technology for addressing hallucination issues in the responses generated by large language models (LLMs). Existing studies on RAG primarily focus on applying semantic-based approaches to retrieve isolated relevant chunks, which ignore their intrinsic relationships. In this paper, we propose a novel Knowledge Graph-Guided Retrieval Augmented Generation (KG$^2$RAG) framework that utilizes knowledge graphs (KGs) to provide fact-level relationships between chunks, improving the diversity and coherence of the retrieved results. Specifically, after performing a semantic-based retrieval to provide seed chunks, KG$^2$RAG employs a KG-guided chunk expansion process and a KG-based chunk organization process to deliver relevant and important knowledge in well-organized paragraphs. Extensive experiments conducted on the HotpotQA dataset and its variants demonstrate the advantages of KG$^2$RAG compared to existing RAG-based approaches, in terms of both response quality and retrieval quality.

Previous work on long-form audio generation using global-view diffusion or iterative generation demands significant training or inference costs. While recent advancements in multi-view joint diffusion for panoramic generation provide an efficient option, they struggle with spectrum generation with severe overlap distortions and high cross-view consistency costs. We initially explore this phenomenon through the connectivity inheritance of latent maps and uncover that averaging operations excessively smooth the high-frequency components of the latent map. To address these issues, we propose Swap Forward (SaFa), a frame-level latent swap framework that synchronizes multiple diffusions to produce a globally coherent long audio with more spectrum details in a forward-only manner. At its core, the bidirectional Self-Loop Latent Swap is applied between adjacent views, leveraging stepwise diffusion trajectory to adaptively enhance high-frequency components without disrupting low-frequency components. Furthermore, to ensure cross-view consistency, the unidirectional Reference-Guided Latent Swap is applied between the reference and the non-overlap regions of each subview during the early stages, providing centralized trajectory guidance. Quantitative and qualitative experiments demonstrate that SaFa significantly outperforms existing joint diffusion methods and even training-based long audio generation models. Moreover, we find that it also adapts well to panoramic generation, achieving comparable state-of-the-art performance with greater efficiency and model generalizability. Project page is available at https://swapforward.github.io/.

The Parry-Lord oral-formulaic theory was a breakthrough in understanding how oral narrative poetry is learned, composed, and transmitted by illiterate bards. In this paper, we provide an annotated reading of the mechanism underlying this theory from the lens of large language models (LLMs) and generative artificial intelligence (AI). We point out the the similarities and differences between oral composition and LLM generation, and comment on the implications to society and AI policy.

While there has been much research into developing AI techniques for fake news detection aided by various benchmark datasets, it has often been pointed out that fake news in different geo-political regions traces different contours. In this work we uncover, through analytical arguments and empirical evidence, the existence of an important characteristic in news originating from the Global South viz., the geo-political veracity gradient. In particular, we show that Global South news about topics from Global North -- such as news from an Indian news agency on US elections -- tend to be less likely to be fake. Observing through the prism of the political economy of fake news creation, we posit that this pattern could be due to the relative lack of monetarily aligned incentives in producing fake news about a different region than the regional remit of the audience. We provide empirical evidence for this from benchmark datasets. We also empirically analyze the consequences of this effect in applying AI-based fake news detection models for fake news AI trained on one region within another regional context. We locate our work within emerging critical scholarship on geo-political biases within AI in general, particularly with AI usage in fake news identification; we hope our insight into the geo-political veracity gradient could help steer fake news AI scholarship towards positively impacting Global South societies.

Synthetic data generation is widely recognized as a way to enhance the quality of neural grammatical error correction (GEC) systems. However, current approaches often lack diversity or are too simplistic to generate the wide range of grammatical errors made by humans, especially for low-resource languages such as Arabic. In this paper, we will develop the error tagging model and the synthetic data generation model to create a large synthetic dataset in Arabic for grammatical error correction. In the error tagging model, the correct sentence is categorized into multiple error types by using the DeBERTav3 model. Arabic Error Type Annotation tool (ARETA) is used to guide multi-label classification tasks in an error tagging model in which each sentence is classified into 26 error tags. The synthetic data generation model is a back-translation-based model that generates incorrect sentences by appending error tags before the correct sentence that was generated from the error tagging model using the ARAT5 model. In the QALB-14 and QALB-15 Test sets, the error tagging model achieved 94.42% F1, which is state-of-the-art in identifying error tags in clean sentences. As a result of our syntactic data training in grammatical error correction, we achieved a new state-of-the-art result of F1-Score: 79.36% in the QALB-14 Test set. We generate 30,219,310 synthetic sentence pairs by using a synthetic data generation model.