Look Up Trending Now Good Connection: Uplifting stories for a digital age Creator Playbook Say More Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Switch Off Mashable Voices Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List All Series Nvidia's Hugging Face purchase confirmed at $13 billion The open source platform that was recently hacked by rogue OpenAI agents is now owned by the AI industry leader. The open source AI platform Hugging Face has been acquired by Nvidia. It's official: Nvidia has acquired the open-source AI platform Hugging Face for $13 billion. Or, according to Nvidia CEO Jensen Huang, the acquisition was for $12,930,300,000, to be exact. In a post on Nvidia's website on Thursday, Huang confirmed that the chipmaker turned AI industry giant has officially purchased Hugging Face after reports said an acquisition was in the making last week.

The unexpected interruption came hours before the president wrote a congratulatory post on Truth Social about the company's most recent earnings report. US President Donald Trump listens as Nvidia CEO Jensen Huang speaks at the White House during an event on "Investing in America" on April 30, 2025 in Washington, DC. President Donald Trump interrupted Nvidia's all-hands staff meeting on Thursday morning when he called CEO Jensen Huang midway through, three sources who witnessed the interaction tell WIRED. Nvidia's all-hands meetings typically involve Huang presenting or answering questions, on stage, to in-person staff at the company's Santa Clara, California, headquarters, while thousands of remote employees tune in to a virtual livestream. During the meeting on Thursday, Huang received a call on his mobile phone, which he stepped aside to take.

Say More Look Up Switch Off Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Creator Playbook Mashable Voices Trending Now Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List In My Bag All Series Nvidia CEO Jensen Huang says AGI is already here -- and the milestone is'senseless' Nvidia's focus is now on'productive work' and'profitable tokens.' Nvidia CEO Jensen Huang says the company has already achieved AGI but it doesn't matter. Some AI industry leaders, such as OpenAI CEO Sam Altman, are obsessed with artificial general intelligence, or AGI. While the term has no universally agreed-upon definition, it generally refers to AI that can match or even surpass human cognitive abilities across a large range of tasks. In a recent interview, Altman shared that OpenAI believes it will achieve AGI by the end of the year.

Modern engineering, spanning electrical, mechanical, aerospace, civil, and computer disciplines, stands as a cornerstone of human civilization and the foundation of our society. However, engineering design poses a fundamentally different challenge for large language models (LLMs) compared with traditional textbook-style problem solving or factual question answering. Although existing benchmarks have driven progress in areas such as language understanding, code synthesis, and scientific problem solving, real-world engineering design demands the synthesis of domain knowledge, navigation of complex trade-offs, and management of the tedious processes that consume much of practicing engineers' time. Despite these shared challenges across engineering disciplines, no benchmark currently captures the unique demands of engineering design work. In this work, we introduce EngDesign, an Engineering Design benchmark that evaluates LLMs' abilities to perform practical design tasks across nine engineering domains. Unlike existing benchmarks that focus on factual recall or question answering, EngDesign uniquely emphasizes LLMs' ability to synthesize domain knowledge, reason under constraints, and generate functional, objective-oriented engineering designs. Each task in EngDesign represents a real-world engineering design problem, accompanied by a detailed task description specifying design goals, constraints, and performance requirements. EngDesign pioneers a simulation-based evaluation paradigm that moves beyond textbook knowledge to assess genuine engineering design capabilities and shifts evaluation from static answer checking to dynamic, simulation-driven functional verification, marking a crucial step toward realizing the vision of engineering Artificial General Intelligence (AGI).

Instead of injecting 3D representations, we unlock VLMs using spatially relevant 2D images. To this end, we introduce a novel 2D spatial data generation and enables annotation the creation pipeline of a b di uilt verse upon set scene of spatial data tasks, with 3D ranging ground-truth.

Leveraging attention sparsity to accelerate long-context large language models (LLMs) has been of great importance recently. However, most existing sparse attention algorithms use a fixed budget of how many tokens to use in their computations. This simple static decision raises critical issues in real-world deployment because it fails to account for the dynamic nature of real-world scenarios, where the optimal balance between accuracy and efficiency can vary greatly. In this paper, we reveal a key insight that leveraging the idea of top-p sampling (a.k.a., nucleus sampling) in sparse attention could enable efficient and adaptive budget decisions. Based on this, we propose Twilight, a framework that enhances any existing sparse attention algorithm with adaptive budget decision capabilities without sacrificing accuracy. Empirical results show that Twilight can adaptively prune up to 98% tokens with nearly no accuracy loss in both long-and medium-context scenarios, leading to a 1.4 speedup over state-of-the-art sparse attention mechanisms.

Nvidia CEO Jensen Huang says AI will transform the workforce like the Industrial Revolution, creating new jobs even as it reshapes how Americans work.

Reinforcement learning with verifiable rewards (RLVR) has shown promise in enhancing the reasoning capabilities of large language models by learning directly from rule-based outcome rewards. Recent RLVR works that operate under the zero setting avoid supervision in labeling the reasoning process, but still depend on manually curated collections of questions and answers for training. The scarcity of high-quality, human-produced examples raises concerns about the long-term scalability of relying on human supervision, a challenge already evident in the domain of language model pretraining. Furthermore, in a hypothetical future where AI surpasses human intelligence, tasks provided by humans may offer limited learning potential for a superintelligent system. To address these concerns, we propose a new RLVR paradigm called Absolute Zero, in which a single model learns to propose tasks that maximize its own learning progress and improves reasoning by solving them, without relying on any external human or distillation data. Under this paradigm, we introduce the Absolute Zero Reasoner (AZR), a system that self-evolves its training curriculum and reasoning ability. AZR uses a code executor to both validate self-proposed code reasoning tasks and verify answers, serving as an unified source of verifiable feedback to guide open-ended yet grounded learning. Despite being trained entirely without external data, AZR achieves overall SOTA performance on coding and mathematical reasoning tasks, outperforming existing zero-setting models that rely on tens of thousands of in-domain human-curated examples. Furthermore, we demonstrate that AZR can be effectively applied across different model scales and is compatible with various model classes.

As a bio-inspired vision sensor, spike camera records light intensity by accumulating photons and firing a spike once a preset threshold is reached. For high-light regions, the accumulated photons may reach the threshold multiple times within a readout interval, while only one spike can be stored and read out, resulting in incorrect intensity representation and a limited dynamic range. Multi-level (ML) spike camera enhances the dynamic range by introducing a spike-firing counter (SFC) to count spikes within each readout interval for each pixel, and uses different spike symbols to represent the arrival of different amounts of photons. However, when the light intensity becomes even higher, each pixel requires an SFC with a higher bit depth, causing great cost to the manufacturing process. To address these issues, we propose time-encoding (TE) spike camera, which transforms the counting of spikes to recording of the time at which a specific number of spikes (i.e., an overflow) is reached.

Vision-Language-Action (VLA) models are increasingly used for end-to-end driving due to their world knowledge and reasoning ability. Most prior work, however, inserts textual chains-of-thought (CoT) as intermediate steps tailored to the current scene. Such symbolic compressions can blur spatio-temporal relations and discard fine visual cues, creating a cross-modal gap between perception and planning. We propose FSDrive, a visual spatio-temporal CoT framework that enables VLAs to think in images. The model first acts as a world model to generate a unified future frame that overlays coarse but physically-plausible priors--future lane dividers and 3D boxes--on the predicted future image. This unified frame serves as the visual CoT, capturing both spatial structure and temporal evolution.