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It's a great time to explore🎁next-gen tech to help you fast-track #AI and #ML development in 2025! Test the potential of unlimited #syntheticdata generation and reusable workflows to accelerate training, testing, and validation of #computervision systems with Rendered.ai's data engineering platform this holiday season. Start your 30-day free trial now: https://buff.ly/3Vj959E Personalized guidance from Rendered.ai's team of experts and the Rendered.ai #PaaS enable: 🟢 Physically accurate synthetic imagery generation for any sensor type, using your own #3D assets or the assistance of Rendered.ai artists 🟢 Unlimited, 100% accurately labeled data creation for a set subscription price 🟢 Configurable and reusable data generation workflows that allow for faster iterative testing and team collaboration 🟢Direct integrations with best-in-class simulators for domain-specific needs, including NVIDIA Omniverse, Quadridox, Inc. QSIM RT, and Rochester Institute of Technology's DIRSIG. 🟢 Tools to easily comparison of real and synthetic datasets to optimize model training

SAKURA-II Benefits Series #10: Power Management For AI processing at the edge, it is critical to meet specific power requirements in order to create a successful product. Whether the system is battery-based or AC powered, the overall power consumption affects the cost of daily operation, long-term reliability, replacement and repair costs, etc. Even if a GPU or accelerator meets the AI processing requirements, if the device consumes too much power, it will be untenable for edge applications. The SAKURA-II platform operates within a power envelope of just 10W, making it ideal for most edge applications. In addition, SAKURA-II uses built-in, automatic on-chip power gating to minimize power consumption, and offers users the ability to shut down parts of the DNA engines to optimize their system power. Learn more about the platform on our Product Overview page. Download the product briefs here: SAKURA-II AI Accelerator Product Brief; SAKURA-II M.2 Modules Brief; and SAKURA-II PCIe Cards Brief. Empowering Generative AI at the Edge - Unveiling the SAKURA-II Benefit Blogs: https://hubs.li/Q02JJLtb0 Product Overview: https://hubs.li/Q02JJL6L0 SAKURA-II AI Accelerator Product Brief: https://hubs.li/Q02JJL4T0 SAKURA-II M.2 Modules Brief: https://hubs.li/Q02JJLh40 SAKURA-II PCIe Cards Brief: https://hubs.li/Q02JJKZp0

Can You See the Future of Radar Systems and Wireless Communications Technologies? NVIDIA Powered Ansys Perceive EM can. As the demand for high speed connectivity and sensing surges, engineers face the daunting task of optimizing coverage in complex urban environments and large-scale #radar systems. The challenges are immense: mitigating interference, achieving optimal coverage, and managing dynamic user behavior in #wireless networks, as well as ensuring accurate performance in radar systems. Traditional modeling methods often fall short, unable to provide the accuracy needed for modern wireless communication. Synthetic data generation is crucial in this scenario. AI/ML learning algorithms rely on vast amounts of data to optimize system performance and design choices. However, obtaining high-quality, real-world data is difficult and costly. This is where synthetic data, generated through advanced simulations, becomes indispensable. #Ansys Perceive EM, powered by #NVIDIA, addresses these challenges by providing high-fidelity results and on-demand synthetic data generation for #AI /ML applications. Read our recent blog to learn more: https://ansys.me/3xxUf6p Also make sure to register for our upcoming webinar to learn more! Webinar Registration link: https://ansys.me/3KOMwnv #HFSS #antenna #artificialintelligence #RF

LATEST IN AI : Quick Read Nvidia launches Nemotron, a 70B model that outperforms GPT-4o and Claude 3.5 Sonnet. Technical Highlights : The model features 70 billion parameters, offering efficient handling of text and coding queries. It builds on Llama 3.1 architecture, based on transformer technology, ensuring coherent and human-like responses. Performance Benchmarks : Nemotron-70B achieved high scores on alignment benchmarks such as Arena Hard (85.0), AlpacaEval 2 LC (57.6), and GPT-4-Turbo MT-Bench (8.98), surpassing its larger counterparts. Efficiency Focus : Despite having fewer parameters compared to GPT-4o, the model's performance demonstrates the efficiency of smaller, well-optimized models. Open-Source Availability : Nvidia has made the model, reward models, and training datasets open-source on Hugging Face, encouraging further testing and innovation. This launch reinforces Nvidia's growing influence in AI beyond hardware, showcasing the potential of efficient, smaller-scale LLMs. NVIDIA #futureofai #aiinmedicine

As AI models continue to drive up energy demand with projections showing a 160% rise in electricity consumption by 2030, startup Sagence AI is taking a bold step toward a more sustainable solution. Sagence has launched energy-efficient analog chips to run AI models, challenging the dominance of power-hungry GPUs. Unlike traditional digital chips that store data in binary (ones and zeros), Sagence's analog chips can represent data using a range of values, offering higher data density and faster processing speeds. These "in-memory" chips don’t require transferring data between memory and processors, which can reduce bottlenecks and improve performance. Although analog chips face challenges in precision and programming complexity, Sagence aims to complement digital chips, targeting specific applications where speed and energy efficiency are key. With plans to bring their chips to market in 2025, the company has already raised $58 million in funding and is working with several customers to integrate their technology into existing systems. As AI's environmental footprint grows, Sagence’s innovative approach could become a critical player in making AI computing more sustainable and accessible. What are your thoughts on this? Source – TechCrunch Disclaimer – The image is used for educational purposes. No infringement of right is intended. #SagenceAI #AnalogChips #EnergyEfficientAI #SustainableTech #GPUs #AIInnovation #TechDogs

The Energy Demands of AI In the race towards Artificial Intelligence, one crucial element often overlooked is the immense amount of electricity required to power compute clusters. These clusters, comprising high-performance GPUs and specialised AI hardware, are the backbone of modern AI research and deployment. The transition from simple to more complex models has shown an exponential increase in computational requirements, and achieving Artificial General Intelligence (AGI) will demand even more significant power resources. As we push the boundaries of AI capabilities, the need for a robust, scalable, and secure power infrastructure becomes ever more pressing. It’s not just about having the latest technology but ensuring we have the electricity to fuel it. This surge in power demand is akin to a wartime mobilisation, reflecting the urgency and scale of the challenge. What are your thoughts on the energy demands of AI? How can we ensure our power infrastructure keeps pace with technological advancements? Are we ready to invest in the necessary power resources to stay ahead in the global AI race? #AGI #Electricity #TechInnovation #FutureOfAI #Sustainability #EnergyEfficiency #SmartGrid #RenewableEnergy #TechInfrastructure #AIResearch #PowerManagement #GreenTech #DigitalTransformation

🚀 𝗘𝘅𝗰𝗶𝘁𝗶𝗻𝗴 𝗡𝗲𝘄𝘀 𝗶𝗻 𝗔𝗜 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴! 🌍 Engineers at 𝗕𝗶𝘁𝗘𝗻𝗲𝗿𝗴𝘆 𝗔𝗜 have developed a groundbreaking algorithm that could revolutionize AI power consumption. Their new method, 𝗟𝗶𝗻𝗲𝗮𝗿-𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 (𝗟-𝗠𝘂𝗹), 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝘀 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗳𝗹𝗼𝗮𝘁𝗶𝗻𝗴-𝗽𝗼𝗶𝗻𝘁 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝘀𝗶𝗺𝗽𝗹𝗲𝗿 𝗶𝗻𝘁𝗲𝗴𝗲𝗿 𝗮𝗱𝗱𝗶𝘁𝗶𝗼𝗻, achieving results that are comparable in accuracy while drastically reducing power use by up to 95%! W𝗵𝘆 𝗶𝘀 𝘁𝗵𝗶𝘀 𝘀𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁? As AI systems become increasingly demanding, power consumption has emerged as a critical challenge. Last year alone, data center GPUs consumed more energy than over a million homes. This new algorithm not only promises to alleviate that strain but could also allow for sustainable AI advancements without compromising our environmental goals. However, as promising as this development is, the existing hardware, like Nvidia’s upcoming Blackwell GPUs, isn't currently optimized for this algorithm. This presents a dilemma for companies that have heavily invested in traditional AI infrastructure. Yet, the potential benefits of L-Mul could spur the creation of specialized chips designed to harness this innovation. In a world where AI capabilities are growing rapidly, it's essential to prioritize efficiency alongside performance. If L-Mul delivers on its promises, we may find ourselves at the cusp of a new era in AI—one where we can advance technology while protecting our planet. Let’s keep our fingers crossed for the future of AI! 🤖✨ https://lnkd.in/d_X4VFiE #AI #Innovation #Sustainability #TechNews #EnergyEfficiency

With the power of Nvidia Omniverse virtual environments and Edge Impulse, engineers can rapidly add photo-realistic training images to computer vision datasets.  Learn how to quickly augment your machine learning projects with synthetic data generated in Omniverse, here: https://lnkd.in/gx3kwBCD NVIDIA AI NVIDIA Omniverse #MachineLearning

📰 Tech News Snippet #587 Exciting developments are sparking interest in the tech industry. Primate Labs has unveiled its latest innovation: Geekbench AI. This advanced benchmarking tool is tailored for AI-centric workloads and machine learning tasks. Geekbench AI 1.0 is the result of years of dedicated development and collaboration with industry experts. It offers precise metrics to evaluate AI performance, now aligning with industry standards. As AI continues to drive technological progress, tools like Geekbench AI are crucial for companies aiming to optimize their AI operations. This launch could indirectly influence NVIDIA, as their GPUs are widely used in AI applications. With AI workloads requiring robust processing power, demand for high-performing GPUs might see an uptick. This could have a potential impact on NVIDIA's stock performance, as the company is a leader in the AI hardware space. Stay informed as these advancements unfold, reinforcing the continuous growth in AI technology and its implications on the market. #NVIDIA #TechNews #AI #Benchmarking #Investing

#YOLOv9 A Leap Forward in object detection Technology #YOLOv9 marks a significant advancement in real-time object detection, introducing groundbreaking techniques such as Programmable Gradient Information PGi and the Generalized Efficient Layer Aggregation Network #GELAN. This model demonstrates remarkable improvements in efficiency, accuracy, and adaptability, setting new benchmarks on the #COCO dataset. The YOLOv9 project, while developed by a separate open-source team, builds upon the robust codebase provided by Ultralytics #YOLOv5, showcasing the collaborative spirit of the AI research community. Fiverr OpenCV Microsoft Muhammad Rizwan Munawar Amazon LinkedIn Google Roboflow CVAT.AI NVIDIA NVIDIA Taiwan Rapidev CAF (Construcciones y Auxiliar de Ferrocarriles) YOLOvX Apple IBM Glenn Jocher Andrew Ng INOA Ivan APEDO SpaceX Tesla Tesla Xeven Solutions Ritesh Kanjee European Commission MathWorks Raptive Sundar Pichai InLights If you have any issue in development then text me on what's app +923125094307