Company Overview & Strategy - The firm is a life sciences venture fund with over 50 years of history, investing in biotechnologies, medical devices, industrial biotech, and digital medicine [1][2] - The company's mission is to advance medicine and bring expertise to the industrial biotechnology field [2] - The fund strategy sits at the intersection of life sciences and technology, bridging traditional life science approaches with digital medicine [4] Portfolio & Partnerships - The firm has connections with about 100 active portfolio companies [2] - The company emphasizes the benefits of connecting portfolio companies with Nvidia, streamlining processes and improving analysis speed [3] - The NVIDIA VC Alliance program connects the firm with other VCs across the ecosystem and industry, providing exposure to the tech side [3][4] Competitive Advantage & Value Proposition - The company provides portfolio companies with exclusive access to compute resources, offering a competitive advantage [6] - The firm offers the ability to tweak compute requirements as needed, which is valuable for startups building massive data assets [7] - The company enables portfolio companies to scale data processing quickly, accelerating the path to value inflection points [8][9]

Industrial Revolution & Technological Advancement - The UK was the birthplace of the industrial revolution, driven by scarce and costly labor, leading to invention [1] - Steam, electricity, and computing each represent a revolution rewriting human progress [3] - Watt's engine, Darby and Cort's iron forging, and Arkwright's looms significantly enhanced industrial capabilities [2] - Faraday and Maxwell's work on electricity and magnetism connected nations and multiplied human output [2] AI Revolution - The AI revolution is powered by Nvidia, manufacturing tokens of intelligence [3] - AI automates mental work, reshaping every industry and field of science [4] - Nations are expected to build AI infrastructure similar to railways, power grids, and the internet [4] - The age of AI has begun, promising a world that will never be the same [4] Historical Context & Future Implications - The UK, where the industrial revolution began, is positioned to be at the forefront of the AI revolution [4] - The AI revolution is presented as a new kind of factory, automating the work of the mind [3][4]

AI is transforming the way that we understand and treat diseases. And nowhere is this more evident than in how we study proteins. It used to take months of lab experiments to determine the structure of a protein.With the release of AlphaFold 2 in 2021. That process can be reduced to minutes with deep learning. This capability can now fit on your desk.The NVIDIA DGX Spark brings data center class performance to protein AI, powered by the Grace Blackwell architecture, with up to one Petaflop of compute and 12 ...

Industrial AI Adoption & Market Opportunity - Industrial AI 正在经历极端的采用 [1] - NVIDIA 认为物理 AI 和工业 AI 是一个 10 万亿美元的行业 [2] - 工业 AI 不仅仅是数字孪生，它贯穿整个汽车生命周期，从前期设计协作到性能数字孪生 [4] - 工业 AI 被注入到数字孪生的创建中 [14] - 工业 AI 的创建从两个角度考虑：使用 Omniverse 创建工厂或仓库的虚拟版本，并使用 Cosmos 创建无限的变体 [15][16] Digital Twin Transformation - 行业正在从规划数字孪生转型到运营数字孪生，并注入 AI [5][8] - 运营数字孪生中，AI 可用于预测性维护，实时零件规划和机器人路径规划 [7] - 通过 AI 协同，用户可以通过提问来监控生产量，生产线问题等 [12][13] Mobility & Virtual Experience - 使用虚拟配置器查看不同的颜色和装饰组合 [9] - 能够实时体验车辆在不同环境条件下的性能，并最终通过 XR 体验坐在车辆中 [10] - 可以在 XR 中体验自动驾驶的未来 [10] AI-Powered Tools & Platforms - Metropolis 平台连接数千个摄像头，以帮助识别数字孪生如何实时运行 [22] - Isaac 和 Groot 平台用于机器人开发，帮助训练和测试 AMR，AGV，ARM 和人形机器人 [22] - Blueprint 旨在解决查找符合特定场景的数据，创建无限变体并基于数据训练模型的难题 [20][21] Real-World Applications & Partnerships - 已经与 BMW，Keon，Kanti，Rockwell Automation 和 Foxconn 合作实现了数字孪生 [5][25][26] - 与 Accenture 合作，将技术应用于 Keon 的仓库，实时感知数字孪生的每个操作 [24] - 设想与 Service Now 建立深度合作伙伴关系，在工厂车间触发实时警报，从而调试机器人或人员来处理事件 [19]

Modeling and simulation are core pillars of science and engineering workflows. AI surrogate models can enable nearrealtime simulation workflows. To develop such models, it's critical to integrate domain knowledge.But building these knowledge guided models isn't easy. Training a model only on available data restricts its usability as it has very limited generalizability. Existing frameworks also don't cater to approaches that fuse data with domain knowledge.NVIDIA Physics Nemo is an open-source AI physics pl ...

NVIDIA's AI Solutions for Automotive - NVIDIA pioneers accelerated computing, assisting companies with AI transformation, particularly in the automotive sector, collaborating with almost all automakers [1] - NVIDIA's solutions span various automotive GenAI use cases, including enterprise transformation, dealership support, in-cabin experience enhancement, self-driving technology, and design/manufacturing improvements [2] - NVIDIA provides an AI factory encompassing compute, software, and tools to handle diverse AI verticals like generative AI, physical AI, and HPC workflows [4] - NVIDIA's AI factory offers optimized use cases with a lower Total Cost of Ownership (TCO) [5] Enterprise AI Applications - Only 1% of enterprises have mature AI deployments, indicating a significant adoption challenge that NVIDIA aims to address with its ecosystem of partners [4] - NVIDIA is helping companies set up IT hubs for employee efficiency, connected vehicle data management, factory floor optimization, and code/test case generation [5] - NVIDIA assists customers with the V-model for design and development, introducing AI agents to improve requirements management [6][7][8] - NVIDIA partners with ServiceNow to introduce AI agents on the factory floor, cutting resolution time from hours to minutes, potentially saving customers hundreds of thousands of dollars per minute [8][9][10][11][12] In-Vehicle AI Applications - China is leading in in-vehicle AI innovation, focusing on personalization, multimodality, companion features, and sentry mode [13][14] - NVIDIA provides solutions for developing AI agents that operate both inside the vehicle and in the cloud, enabling features like predictive maintenance and roadside assistance [16][17][18][19] - NVIDIA offers full-stack platforms, including CUDA, to ensure seamless transition and migration from cloud to car, allowing customers to differentiate their end products [21][22] - Ford is using AI agents for design (V-model), contact center customer service, and in-vehicle repair manuals, demonstrating significant time savings (hours to seconds) [26][27]

Proteins are the machines of life. Understanding how proteins fold and interact with other molecules is a key part of understanding humanity's most pressing biological challenges. From designing new drugs to growing safer, healthier food, and even designing new cosmetics, resolving the 3D structure of proteins once took years in the lab.AI now makes this possible in minutes. With the NVIDIA RTX Pro 6000 Blackwell Server Edition, protein structure inference is over 100 times faster than before, unlocking the ...

AI has truly crossed the chasm from back office proof of concepts to full mainstream production. And the way that AI is getting deployed and used is through inference. So when we think about inference, it's actually two workloads.There's the context processing which we call prefill and then there's the generation of tokens which we call decode. In order to serve those two disparate processes more efficiently, the industry has developed disagregated serving. It's a method by which you can basically separate ...

General Overview - The document is a musical piece or a reference to music [1]

NVIDIA accelerated computing starts with DGX, the world's AI supercomputer, the engine behind the large language model breakthrough. IHand delivered the world's first DGX to open AI. Since then, half of the Fortune 100 companies have installed DGX AI supercomputers. DGX has become the essential instrument of AI. The GPU of DGX is eight H100 modules.H100 has a transformer engine designed to process models like the amazing chat GPT which stands for generative pre-trained transformers. The eight H100 modules a ...