AI Native Wireless Networks Overview - NVIDIA AI Aerial is revolutionizing wireless systems by enabling neural networks to learn from data, adapt in real-time, and scale with complexity [1] - This approach boosts spectral efficiency, lowers costs, and improves connectivity in wireless networks [1] Key Steps & Technologies - Building AI native wireless networks involves three key steps: training, simulation, and deployment, all unified by NVIDIA AI Aerial [2] - NVIDIA uses Shauna and Aerial radio frameworks to train neural network models, replacing traditional 5G channel estimation algorithms [2] - The trained model is deployed on a live 5G network at NVIDIA using Aerial RAN computer [3] - NVIDIA provides the Shona research kit on NVIDIA Jetson, enabling developers to create a working AI model in a live 5G network with minimal code [5] Performance & Results - AI implementation nearly doubles the cell throughput [3] - AI achieves 100% higher throughput on a live 5G network [4] - The AI model continuously improves over time through a feedback loop between the physical and virtual worlds [4] Future Implications - Improved AI models will boost capacity, unlock new applications, increase efficiency, and extend coverage [5] - NVIDIA encourages participation in the 6G developer program to accelerate AI native wireless research [5]

AI Agents Overview - AI agents are digital assistants that use prompts to reason and break down problems into multi-step plans [1] - They utilize appropriate tools, collaborate with other agents, and leverage context from memory on NVIDIA accelerated systems [1] - The process begins with a simple prompt, exemplified by asking Perplexity to initiate a food truck business in Paris [1] Agent Collaboration and Functionality - Agents collaborate to address each step, utilizing various tools [2] - A market researcher analyzes trends and the competitive landscape through reviews and reports [2] - A concept designer explores local ingredients and proposes a menu with prep time estimates, researches pallets, and develops a brand identity [2] - A financial planner employs Monte Carlo simulations to project profitability and growth [3] - An operations planner creates a detailed launch timeline, covering equipment procurement and permit acquisition [3] - A marketing specialist develops a launch plan with a social media campaign and an interactive website featuring a map, menu, and online ordering [3] Proposal and Outcome - The collective work of each agent culminates in a final packaged proposal [3] - All of this originates from a single prompt [3]

Core Offering - DJX Cloud Leptin provides on-demand access to a global network of GPUs across clouds, regions, and partners [1] - Multicloud GPU clusters are managed through a single unified interface [1] - Connects developers to GPU compute, powering a virtual global AI factory [3] Key Features - Fast provisioning allows developers to quickly scale up the number of nodes without complex setups [2] - Pre-integrated tools and training-ready infrastructure enable developers to start training immediately [2] - Real-time progress monitoring provides insights into GPU performance, convergence, and throughput [2] - Supports deployment of NIM endpoints or models in multiple clouds or regions for fast distributed inference [2]

Platform Overview - NVIDIA Omniverse is a platform comprising APIs, SDKs, and services [1] - The platform facilitates the integration of OpenUSD, NVIDIA RTX rendering technologies, and generative PhysicalAI [1] - It targets industrial and robotic use cases by enhancing software tools and simulation workflows [1] Key Technologies - OpenUSD integration is a core feature of the platform [1] - NVIDIA RTX rendering technologies are incorporated [1] - Generative PhysicalAI is supported for advanced simulations [1] Target Audience - The platform is designed for developers [1] - It aims to improve existing software tools [1] - It supports simulation workflows in industrial and robotic sectors [1]

Autonomous Vehicle Safety - NVIDIA Drive, built on the Halo safety system, enables developers to build safe autonomous vehicles with diverse software stacks, sensors, and redundant computers [1] - Safe AVs require massive amounts of diverse data to address edge cases, which are supplemented by realistic synthetic training data generated using NVIDIA Omniverse and Cosmos [2] - An independent classical stack runs in parallel for decision-making diversity, with guardrails monitoring safe performance and an arbitrator for emergency stops [3] Redundancy and Reliability - Each sensor connects to redundant computers, ensuring vehicle safety and operation even if a sensor or computer fails [4] - In the event of a critical failure, the system can execute a minimum risk maneuver, such as pulling over to the shoulder [4] Technology and Integration - NVIDIA Drive allows developers worldwide to integrate Halo into their products to build the next generation of safe AVs [4]

Smart City AI & Infrastructure - 到 2050 年，全球三分之二的人口将居住在城市，对基础设施造成更大压力 [1] - 物理人工智能 (Physical AI) 可以帮助改善数十亿人的生活 [1] - 英伟达 Omniverse 智能城市 AI 蓝图通过在一个统一的过程中结合数字孪生、训练和部署，使城市规模的 AI 成为可能 [2] Technology & Development - 开发人员必须模拟真实世界的条件，并实时处理数十亿个传感器输入和数百万个摄像头流 [1] - 里程碑系统 (Milestone Systems) 从 NVIDIA Cosmos 生成合成视频，并使用 Nemo Curator 处理和定制他们的数据集 [3] - K2K 使用定制模型来构建 AI 代理，以帮助解决整个城市的特定交通和安全问题 [4] - 借助 NVIDIA Omniverse 智能城市 AI 蓝图，开发时间几乎缩短了一半 [4] Use Case & Application - 以交通拥堵为例，管理流量和提高安全性需要实时数据和理解复杂环境的 AI [2] - 在巴黎，Avis 将航空镜头转化为模拟数字孪生 [3]

Industry Revolution & Automation - The first industrial revolution introduced automation through Watt's steam engine and the mechanized loom [1] - The fourth industrial revolution, driven by AI, is reimagining industry [2] - Industrial AI is being implemented from design to engineering [2] Technological Advancements - Electromagnetism was unraveled by Ampier [1] - Faraday built the first electric generator [1] - Maxwell laid the foundations for modern electrical engineering [1] Robotics & Future Workforce - The industry is building the next frontier where everything that moves is robotic, including intelligent autonomous vehicles [3] - Robots are being trained in digital twin worlds and robot gyms to work alongside humans in factories, warehouses, operating rooms, and homes [3] - Collaborative workforce is helping to close the global labor shortage gap [3]

So within deoid we certify our people and we do this for thousands of people every year. We are developing the actual project. We are doing the consultancy and we are also educating the client so he can adopt the deliveries that we provide for them.Nvidia was the first one to adopt AI really promote AI and where our future is headed to whether it's on the actual developments that we provide to our clients eventually or whether it's also the aspect of the DI trainings since the first day I saw the advantage ...