Looking for NYT Connections answers and hints? Here's all you need to know to solve today's game, plus my commentary on the puzzles.

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The U.K. on Monday laid out plans to become a leader in AI, including ambitions to build a homegrown to global AI success stories like OpenAI.

Yarn, commonly understood as spun short fibers, has traditionally been a cornerstone of textiles and crafts. The YYYarn collection reimagines this versatile material, transforming it into a stunning array of unique furniture pieces. By innovatively adapting the classic three-strand braiding knot into a singular yarn design, the collection breaks new ground in modern furniture design while honoring the enduring legacy of traditional craftsmanship.Designers: Choi Piljae,Baek In-ho,Kmuid, andYegeun JoThe YYYarn collection features a sofa, table, and stool, each embodying the essence of woven artistry. With its adaptable design, the collections signature technique allows for diverse applications and creative possibilities:The table consists of a woven piece that serves as the base, supporting a marble or terrazzo top to create a functional and visually stunning table. The sturdy top contrasts beautifully with the soft, braided base, achieving a perfect balance of textures. The sofa is crafted for ultimate comfort, this seating piece uses large woven fibers to provide a soft, inviting experience. Ideal for recreational spaces such as offices, educational institutions, or home theaters, it combines style and relaxation. The stool is compact yet elegant, this piece encapsulates the YYYarn aesthetic, making it a versatile addition to smaller spaces without sacrificing charm or functionality.The YYYarn collection isnt just about functionality, its about making a statement. The bright colors and bold forms inject a playful, quirky aesthetic into any room, making these pieces conversation starters as much as practical furniture. The soft, oversized fibers ensure exceptional comfort, while the designs inherent versatility offers endless opportunities for exploring new forms and shapes. The woven yarn adapts to create a variety of unique configurations, showcasing the potential for innovation within the craft.Available in three vibrant colors; brown, orange, and white, the collection provides a range of options to suit different interior styles. Yet, the potential for customization is vast. With the modular nature of its design, YYYarn opens the door to an expanded palette and new applications, allowing users to personalize their spaces with creativity and flair. By blending artistry, comfort, and functionality, the collection bridges the gap between craft and contemporary design. Whether placed in a professional setting or a personal space, YYYarn redefines what it means to create furniture thats both beautiful and meaningfulThe post Innovative Braided Furniture Merges Craftsmanship and Modern Elegance first appeared on Yanko Design.

Crime in the world of Apple continues with bad guys misusing AirTags in Florida, while others elsewhere use them for good. A few thousand in merchandise were stolen in California, and a disco ball was taken with an iPad in Nashville.The Brent Cross Apple Store in London The latest in an occasional AppleInsider feature, looking at the world of Apple-related crime. Continue Reading on AppleInsider | Discuss on our Forums

The fork-tailed flycatcher (Tyrannus savana) is a bird in the family Tyrannidae, the tyrant flycatchers. Named after their distinguishably long, forked tails, particularly in males, fork-tailed flycatchers are seen in shrubland, savanna, lightly forested and grassland areas, from southern Mexico south to Argentina. They tend to build their cup nests in similar habitats to their hunting grounds (riparian forests and grasslands). Males perform aerial courtship displays to impress females involving swirling somersaults, twists, and flips, all partnered with their buzzing calls. These courtship displays utilise the long tail feathers. This male fork-tailed flycatcher of the subspecies T.s.monachus was photographed in Cayo District, Belize, demonstrating its characteristic forked tail while in flight.Photograph credit: Charles J. SharpRecently featured: John Henry TurpinTocopilla railwayColias croceusArchiveMore featured pictures

January 13: Eugenio Mara de Hostos's birthday in Puerto Rico (2025); Saint Knut's Day in Finland and SwedenWilliam Price1884 Welsh physician William Price (pictured) was arrested for attempting to cremate his deceased infant son; this eventually led to the passing of the Cremation Act 1902 by Parliament.1953 Nine Moscow doctors were accused of a plot to poison members of the Soviet political and military leadership.1968 American singer Johnny Cash recorded his landmark album At Folsom Prison live at Folsom State Prison in California.1972 Ghanaian military officer Ignatius Kutu Acheampong led a coup to overthrow Prime Minister Kofi Abrefa Busia and President Edward Akufo-Addo.2000 Steve Ballmer replaced Bill Gates as the chief executive officer of Microsoft.Edmund Spenser (d.1599)Art Ross (b.1885or1886)Michael Bond (b.1926)Claudia Emerson (b.1957)More anniversaries: January 12January 13January 14ArchiveBy emailList of days of the yearAbout

Large language models (LLMs) like GPT-4, PaLM, Bard, and Copilot have made a huge impact in natural language processing (NLP). They can generate text, solve problems, and carry out conversations with remarkable accuracy. However, they also come with significant challenges. These models require vast computational resources, making them expensive to train and deploy. This excludes smaller businesses and individual developers from fully benefiting. Additionally, their energy consumption raises environmental concerns. The dependency on advanced infrastructure further limits their accessibility, creating a gap between well-funded organizations and others trying to innovate.What are Small Language Models (SLMs)?Small Language Models (SLMs) are a more practical and efficient alternative to LLMs. These models are smaller in size, with millions to a few billion parameters, compared to the hundreds of billions found in larger models. SLMs focus on specific tasks, providing a balance between performance and resource consumption. Their design makes them accessible and cost-effective, offering organizations an opportunity to harness NLP without the heavy demands of LLMs. You can explore more details in IBMs analysis.Technical Details and BenefitsSLMs use techniques like model compression, knowledge distillation, and transfer learning to achieve their efficiency. Model compression involves reducing the size of a model by removing less critical components, while knowledge distillation allows smaller models (students) to learn from larger ones (teachers), capturing essential knowledge in a compact form. Transfer learning further enables SLMs to fine-tune pre-trained models for specific tasks, cutting down on resource and data requirements.Why Consider SLMs?Cost Efficiency: Lower computational needs mean reduced operational costs, making SLMs ideal for smaller budgets.Energy Savings: By consuming less energy, SLMs align with the push for environmentally friendly AI.Accessibility: They make advanced NLP capabilities available to smaller organizations and individuals.Focus: Tailored for specific tasks, SLMs often outperform larger models in specialized use cases.Examples of SLMsLlama 3 8B(Meta)Qwen2: 0.5B, 1B, and 7B (Alibaba)Gemma 2 9B(Google)Gemma 2B and 7B(Google)Mistral 7B(Mistral AI)Gemini Nano 1.8B and 3.25B(Google)OpenELM 270M, 450M, 1B, and 3B(Apple)Phi-4 (Microsoft)and many more..Results, Data, and InsightsSLMs have demonstrated their value across a range of applications. In customer service, for instance, platforms powered by SLMslike those from Aiseraare delivering faster, cost-effective responses. According to an DataCamp article, SLMs achieve up to 90% of the performance of LLMs in tasks such as text classification and sentiment analysis while using half the resources.In healthcare, SLMs fine-tuned on medical datasets have been particularly effective in identifying conditions from patient records. A Medium article by Nagesh Mashette highlights their ability to streamline document summarization in industries like law and finance, cutting down processing times significantly.SLMs also excel in cybersecurity. According to Splunks case studies, theyve been used for log analysis, providing real-time insights with minimal latency. ConclusionSmall Language Models are proving to be an efficient and accessible alternative to their larger counterparts. They address many challenges posed by LLMs by being resource-efficient, environmentally sustainable, and task-focused. Techniques like model compression and transfer learning ensure that these smaller models retain their effectiveness across a range of applications, from customer support to healthcare and cybersecurity. As Zapiers blog suggests, the future of AI may well lie in optimizing smaller models rather than always aiming for bigger ones. SLMs show that innovation doesnt have to come with massive infrastructureit can come from doing more with less.Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit. FREE UPCOMING AI WEBINAR (JAN 15, 2025): Boost LLM Accuracy with Synthetic Data and Evaluation IntelligenceJoin this webinar to gain actionable insights into boosting LLM model performance and accuracy while safeguarding data privacy.The post What are Small Language Models (SLMs)? appeared first on MarkTechPost.

Multi-modal Large Language Models (MLLMs) have revolutionized various image and video-related tasks, including visual question answering, narrative generation, and interactive editing. A critical challenge in this field is achieving fine-grained video content understanding, which involves pixel-level segmentation, tracking with language descriptions, and performing visual question answering on specific video prompts. While state-of-the-art video perception models excel at tasks like segmentation and tracking, they lack open-ended language understanding and conversation capabilities. Moreover, video MLLMs demonstrate strong performance in video comprehension, and question answering but fall short in handling perception tasks and visual prompts.Existing attempts to address video understanding challenges have followed two main approaches: MLLMs and Referring Segmentation systems. MLLMs initially focused on developing improved multi-modal fusion methods and feature extractors, eventually evolving towards instruction tuning on LLMs with frameworks like LLaVA. Recent developments have attempted to unify image, video, and multi-image analysis in single frameworks, such as LLaVA-OneVision. In parallel, Referring Segmentation systems have progressed from basic fusion modules to transformer-based methods, that integrate segmentation and tracking inside videos. However, these solutions lack comprehensive integration of perception and language understanding capabilities.Researchers from UC Merced, Bytedance Seed, Wuhan University, and Peking University have proposed Sa2VA, a groundbreaking unified model designed for a dense grounded understanding of images and videos. The model differentiates itself by supporting a comprehensive range of image and video tasks through minimal one-shot instruction tuning, overcoming the limitations of existing multi-modal large language models. Sa2VAs innovative approach integrates SAM-2, with LLaVA, unifying text, image, and video into a shared LLM token space. The researchers have also introduced Ref-SAV, an extensive auto-labeled dataset containing over 72K object expressions in complex video scenes, with 2K manually validated video objects to ensure robust benchmarking capabilities.Sa2VAs architecture integrates two main components: a LLaVA-like model and SAM-2, connected through a novel decoupled design. The LLaVA-like component consists of a visual encoder processing images and videos, a visual projection layer, and an LLM for text token prediction. The system employs a unique decoupled approach where SAM-2 operates alongside the pre-trained LLaVA model without direct token exchange, maintaining computational efficiency and enabling plug-and-play functionality with various pre-trained MLLMs. The key innovation lies in the connection mechanism using a special [SEG] token, allowing SAM-2 to generate segmentation masks while enabling gradient backpropagation through the [SEG] token to optimize the MLLMs prompt generation capabilities.The Sa2VA model achieves state-of-the-art results on referring segmentation tasks, with Sa2VA-8B scoring 81.6, 76.2, and 78.9 cIoU on RefCOCO, RefCOCO+, and RefCOCOg respectively, outperforming previous systems like GLaMM-7B. In conversational capabilities, Sa2VA shows strong performance with scores of 2128 on MME, 81.6 on MMbench, and 75.1 on SEED-Bench. The model excels in video benchmarks, surpassing previous state-of-the-art VISA-13B by substantial margins on MeVIS, RefDAVIS17, and ReVOS. Moreover, Sa2VAs performance is noteworthy considering its smaller model size compared to competitors, showing its efficiency and effectiveness across both image and video understanding tasks.In this paper, researchers introduced Sa2VA which represents a significant advancement in multi-modal understanding by successfully integrating SAM-2s video segmentation capabilities with LLaVAs language processing abilities. The frameworks versatility is shown through its ability to handle diverse image and video understanding tasks with minimal one-shot instruction tuning, addressing the long-standing challenge of combining perception and language understanding. Sa2VAs strong performance across multiple benchmarks, from referring segmentation to conversational tasks, validates its effectiveness as a unified solution for a dense, grounded understanding of visual content marking a significant step forward in the multi-modal AI systems field.Check out Twitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit. Sajjad Ansari+ postsSajjad Ansari is a final year undergraduate from IIT Kharagpur. As a Tech enthusiast, he delves into the practical applications of AI with a focus on understanding the impact of AI technologies and their real-world implications. He aims to articulate complex AI concepts in a clear and accessible manner. [Recommended Read] Nebius AI Studio expands with vision models, new language models, embeddings and LoRA (Promoted)