Tony Emsenhuber’s Post

I was fortunate to attend Siggraph, a conference on computer graphics and generative AI. Jensen Huang, CEO of NVIDIA, gave a keynote address at the conference. He discussed NVIDIA's journey from a computer graphics company to a leader in accelerated computing and generative AI. Democratizing these tech advancements to general purpose use cases and enabling companies to build capabilities is still a far journey. Some of the thought provoking points that he mentioned are here: - NVIDIA's history: Started in 1993, focused on computer graphics, pioneered GPU technology, and entered the AI market in 2012 with the acquisition of AlexNet. - Accelerated computing: NVIDIA's focus on creating a computing model that could augment general-purpose computing to solve complex problems. - Generative AI: A new way of writing software where machines learn to program themselves, given input and example outputs. - NVIDIA's role: Developing the hardware (GPUs) and software (like CUDA) to power generative AI applications. - Future of AI: More controllable and accurate through techniques like reinforcement learning, guardrails, and retrieval augmented generation (RAG). - Impact on industries: Generative AI will transform various sectors like scientific computing, design, robotics, and customer service. - NVIDIA's strategy: Focus on software development and domain-specific libraries (DSLs) to maintain its lead in accelerated computing. Challenges and opportunities: Addressing ethical concerns around AI, such as potential job displacement and the need for transparency. - NVIDIA's vision: Creating a future where AI assistants augment human capabilities across industries and democratize access to powerful tools. #AI #Trending #GenAI #acceleratedcomputing #GPUs #CUDA

:- NVIDIA's Revolutionary Hardware Paves Way For Future Tech Innovations NVIDIA, often recognized for its graphics cards, is revolutionizing the world with its hardware that powers a range of global tech. According to CEO Jensen Huang, the company's robust technology, including AI, data science, and autonomous machines, are stirring industrial advancements around the world, transforming economies, and reshaping global industries. The titan of the tech world, NVIDIA's graphics processors (GPUs) are not only prevalent in gaming but also in powering AI. Expertly designed, NVIDIA’s chips efficiently perform parallel tasks, making them ideal for training AI algorithms, an exercise that requires crunching vast matrices of data simultaneously. Such efficient parallel processing has made NVIDIA's hardware the preferred choice for many industries, including gaming, cloud computing, self-driving cars, and even healthcare. Jensen Huang argues that as an "accelerator of computation," NVIDIA's tech prowess is driving the latest trends in data science and AI applications. Bearing witness to NVIDIA's pioneering journey, Huang highlights the importance of building something useful that adds value to society. Today, the multinational tech company's AI and machine learning technologies are assuring optimal medical diagnoses, enabling smart cities, and even propelling scientific applications for space exploration. The world's industries are quickly realizing the integral role NVIDIA's hardware plays in their sectors. NVIDIA is indeed proving that its hardware innovations are eating the world, and according to Huang, that’s because they enable "innovations that weren't possible before." Indeed, as Huang notes, every industry is becoming a tech industry and NVIDIA’s hardware is playing a fundamental role in this transformation. #NVIDIA #AI #DataScience. Update as of 23/02/2024 & You can read more about this here: https://lnkd.in/gqnszWUq by WIRED 🔗Follow for more 📈🫡 & Lets connect: https://lnkd.in/dkis_kdf Join MarTech community here: https://tally.so/r/3NWD6B for Exclusive updates. ----------------- #ai #tech

static knowledge vs dynamic knowledge - a collection of established facts vs constantly evolving information The images used to illustrate this post: ·        on the left - Infineon Technologies - SEMPER NOR Flash Memory ·        on the right – Nvidia GeForce 800M series graphics "chip" As a medical student, I remember a conversation I was having with a particularly intelligent fellow student who prided himself on his ability to remember - which helped him get high marks during exams. Exams are about remembering. I conceded that I was not particularly good at remembering - because I had discovered that my brain was designed to act more like a processor - than a memory chip. Exams do not test you on your processing ability (which is the reason that although I managed to pass my exams, I did not achieve high marks). As I explain, this “more processor than memory chip” served me well – both as a pediatric surgeon (originating knowledge and innovating) and as a Health Tourism knowledge and innovation originator. Going back to "computing", memory chips are "dirt cheap", whereas the price of specialized processing units (PUs) like those designed by NVIDIA, have skyrocketed and are difficult to get hold of. Obviously, memory is valued less than processing power. I believe the same holds true in the case of Health Tourism. The observation led me to consider the issue “knowledge” in the context of Health Tourism. As a consequence of this exercise, I concluded that “memory” can be thought of as “static knowledge”, whereas “processing ability” enables one to elevate static knowledge to the status of “dynamic knowledge” – enabling innovation origination (i.e., to originate knowledge). “static knowledge” (which can now, be accessed by all - at any time) provides a “base” (starting point). Stated a little differently, “static knowledge” refers to information that remains constant over time - it's essentially, a collection of established facts – that one can “stand on” in order to create new things (new knowledge - innovations). Which is the reason I have repeatedly claimed that innovation (origination) requires deep (static) knowledge and understanding. From this, one understands that I do not “discount” (i.e., disregard or minimize the importance of) static knowledge. BTW, “dynamic knowledge” is constantly evolving – and refers to information that is not static (fixed). Both (memory and processing ability) are important and “necessary” – but given the choice (of one or the other), I would opt for “processing ability” – any day. #Knowledge #StaticKnowledge #DynamicKnowledge #Origination #Innovation #ProcessingAbility #Memory #KnowledgeOrigination #InnovationOrigination #HealthTourism #ht8 #ContemporaryHealthTourism #Constantinides #EMVIO #Health #LinkedIn #Tourism #Travel #Hospitality #ExCtu #ContemporaryMedicalTourism #MedicalTourism #DentalTourism #SpaTourism #WellnessTourism #SportsTourism #CulinaryTourism #AccessibleTourism #AssistedResidentialTourism

NVIDIA's Accelerated Impact on Metallurgical and Materials Engineering In the dynamic realm of Metallurgical and Materials engineering, technological advancements play a pivotal role in driving innovation. NVIDIA, renowned for its graphics processing units (GPUs) and artificial intelligence technologies, is making significant contributions to the field by enhancing computational capabilities. Here we explore the diverse use cases where NVIDIA's technology is influencing and accelerating progress in Metallurgical and Materials engineering. Use Case 1: Molecular Dynamics Simulations - One of the primary challenges in materials engineering understands the behavior of materials at the atomic and molecular levels. NVIDIA GPUs, with their parallel processing capabilities, excel in accelerating molecular dynamics simulations. Researchers can now simulate the intricate interactions between atoms and molecules with unprecedented speed, enabling a deeper understanding of material properties, phase transitions, and structural changes. Use Case 2: Finite Element Analysis (FEA) for Structural Integrity Metallurgical engineering often involves designing materials with specific structural properties. NVIDIA's GPUs empower engineers to perform complex Finite Element Analysis simulations, predicting how materials respond to various mechanical forces and environmental conditions. This accelerates the optimization of material structures, ensuring enhanced durability and performance in real-world applications. Use Case 3: Materials Genome Initiative NVIDIA's role extends beyond simulations to support initiatives like the Materials Genome Initiative (MGI). This collaborative effort aims to accelerate materials discovery and development using advanced computational tools. By leveraging NVIDIA's GPUs, researchers can analyze vast databases of material properties, speeding up the identification of promising materials for specific applications, from advanced alloys to innovative composites. Use Case 4: AI-Driven Materials Design Artificial intelligence is becoming integral to materials engineering, and NVIDIA's expertise in AI is a game-changer. Machine learning algorithms running on NVIDIA GPUs can analyze vast datasets, identifying patterns and correlations in material properties. This enables the creation of predictive models for material behavior, streamlining the design process and facilitating the development of tailor-made materials for specific industrial needs. Use Case 5: Accelerated Research Collaboration NVIDIA's technology fosters collaboration by enabling researchers to share and process data efficiently. High-performance computing clusters equipped with NVIDIA GPUs facilitate seamless collaboration among scientists and engineers worldwide. This interconnected approach accelerates the pace of research and encourages interdisciplinary efforts to address complex challenges in Metallurgical and Materials engineering. #nvidia #ai #gpu #gpt4 #llms

NVIDIA Driving the AI Revolution #ArtificialIntelligence #AI When it comes to powering the latest advancements in artificial intelligence, few companies are playing a bigger role than NVIDIA. As a leading maker of graphics processing units (GPUs), NVIDIA's hardware provides the computational muscle behind many of today's cutting-edge AI systems and applications. #DeepLearning #NeuralNetworks At the core of NVIDIA's AI strategy are their GPUs optimized for deep learning and neural network training. Their hardware accelerators like the A100 Tensor Core GPU and DGX systems are designed to run the massively parallel computations required for AI workloads. This purpose-built architecture gives NVIDIA a key advantage over traditional CPUs for these AI calculations. #GPGPU #ParallelComputing NVIDIA's CUDA parallel computing platform and optimized AI software libraries like cuDNN and TensorRT have also been instrumental. They allow developers to harness the power of NVIDIA GPUs for a wide range of AI applications from autonomous vehicles and robotics to healthcare and scientific computing. #SelfDrivingCars #Robotics #Healthcare The company has been at the forefront of generative AI as well, with its GPU acceleration being crucial for technologies like DALL-E and large language models. NVIDIA's partnership with Microsoft brought AI to broadly available platforms like Azure AI. #GenerativeAI #AzureAI In the fight against climate change, NVIDIA GPUs are enabling high-resolution climate modeling and drought prediction. And in healthcare, they are accelerating AI for molecular research, COVID-19 vaccine development, and medical imaging analysis. #ClimateChange #COVID19 #MedicalImaging Looking ahead, NVIDIA aims to create an "AI Data Center-in-a-Box" with its CPU, GPU, DPU, and networking products. The goal is an integrated, secure AI data center solution that can be easily deployed from the cloud to the edge. #EdgeComputing #CloudComputing #AIDataCenter As artificial intelligence continues its transformation of industries around the world, NVIDIA is positioning itself as a preeminent provider of the hardware and software building blocks to drive this revolution. With a chip architecture built for the AI future, NVIDIA is powering discoveries and breakthrough applications that will profoundly impact humanity. #InnovationLeaders

NVIDIA CEO Jensen Huang to Discuss AI, Computing, and More at SIGGRAPH 2024 😊 • NVIDIA CEO and founder Jensen Huang will take the stage at SIGGRAPH 2024 in two exciting fireside chats, including a joint discussion with Meta CEO and founder Mark Zuckerberg. • Attendees can expect insights on: - AI-enabled 3D pipelines - Generative AI solutions - Graphics research - Over 45 sessions delivered by NVIDIA and their partners • The conference will also feature: - Skill-enhancing training labs - 23 accepted NVIDIA research papers - NVIDIA Inception Startups Innovation Zone showcasing new use cases for computer graphics, visualization, and AI In an interview, Huang shared: • His excitement about the upcoming keynote, promising news on how AI will transform graphics, robotics, and industries • NVIDIA's pioneering work in accelerated computing and generative AI, which is driving a new industrial revolution and promoting sustainability • Earth-2, NVIDIA's digital twin platform that can accurately predict weather patterns and promote climate research Huang's joint appearance with Zuckerberg will delve into the intersection of research, products, and the future of AI and simulation. 😊 Best career advice Huang received: • Prioritize time and focus on the most important things at work and home #NVIDIA #JensenHuang #SIGGRAPH2024 #AI #Computing #Graphics #Sustainability Read more: https://lnkd.in/dVj9dVQJ

🌐 NVIDIA is introducing the GeForce RTX 40 SUPER Series graphics cards 💳, a significant step 🚀 in AI tech development💡. These cards are powered by the innovative Ada Lovelace architecture, designed specifically to offer trailblazing performance for AI-based neural graphics and ray tracing 🌈. The new GPUs feature dedicated AI Tensor Cores capable of delivering up to 836 AI TOPS (Trillion Operations Per Second). These cores accelerate AI-driven tasks such as deep learning inference, vital for applications including intelligent chatbots 💬, generative AI, natural language processing 📚, and computer vision 👀. Incorporating NVIDIA's Deep Learning Super Sampling (DLSS) technology🔬, a real-time deep learning image enhancement and upscaling solution, these GPUs use AI to produce images with less rendering overhead. As a result, you can expect higher graphical settings and/or frame rates for a given output resolution 📺. Furthermore, the Ada Lovelace architecture's Tensor Cores provide support for structured sparsity and 8-bit floating-point (FP8) precision. This results in up to 4 times higher inference performance in comparison to the previous generation, enabling AI models to be processed more efficiently⚡. Launching in multiple variants with different pricing structures💰 - the RTX 4070 SUPER, RTX 4070 Ti SUPER, and RTX 4080 SUPER - aftermarket GPU options from Asus, MSI, Gigabyte, and Inno3D are also available. This range ensures that varied performance needs and preferences are catered for👍. In conclusion, the RTX 40 SUPER Series serves to enhance AI tech development significantly. By offering powerful AI Tensor Cores for high-performance AI operations, support for advanced AI technologies and applications, and substantial gains in AI inference and neural graphics through the Ada Lovelace architecture, NVIDIA is leading the way in AI development🥇.

Why NVIDIA Leads the AI GPU Race: A Look at Strategy and Software Moats Nvidia's recent stock surge begs the question: how did they become the dominant player in AI GPUs? The answer lies in a series of strategic decisions, starting with their founder, Jensen Huang's, vision. ⭐ From Gaming to General Purpose: Founded in 1993, Nvidia focused on graphics chips for video games. But Mr. Huang saw potential beyond entertainment. In 2006, he introduced CUDA, software allowing programmers to leverage GPUs for tasks beyond graphics, like physics and chemical simulations. This opened the door to a wider market. ⭐ The Deep Learning Breakthrough: 2012 marked a pivotal moment. Researchers achieved human-like image recognition accuracy using GPUs, paving the way for advancements like text-to-image generation. Nvidia seized this opportunity, investing heavily in AI. ⭐ Building an Ecosystem: Over the past decade, Nvidia poured over $30 billion into AI, not just as a component supplier. They collaborated with leading researchers, startups, and built an internal team actively involved in AI activities like language model development. This created a strong ecosystem around their technology. ⭐ The Software Moat: Here's where things get interesting. Popular deep learning libraries like PyTorch and TensorFlow heavily rely on Nvidia's CUDA® Deep Neural Network (cuDNN) library, a GPU-accelerated toolkit for deep neural networks. This, coupled with limited support from competitors like AMD , made Nvidia GPUs the de facto choice for deep learning applications. ⭐ The Monopoly and the Future: This "software moat" has allowed Nvidia to capitalize on the AI boom, leaving competitors like AMD playing catch-up. While AMD is making strides with ROCm, their answer to CUDA, there's a gap in ease of use. Will the Monopoly Last? The short answer is, perhaps not forever. Market forces tend to create competition, and a strong competitor could emerge. But for now, Nvidia's strategic foresight, software ecosystem, and "moat" have them firmly in the lead. #AI #GPU #Nvidia #AMD #DeepLearning #Technology #Innovation