A lot of what goes into figuring out if you like something, is it to be able to rapidly see it as fast as possible. RTX rendering and Unreal Engine is helped. Industrial Light and Magic improve not just the efficiency and speed of storytelling, but it's also increased the quality and the type of answers that you're trying to figure out as a creative.GPU accelerated rendering has really had an impact on how we do reviews. With RTX rendering, we're able to have real time reviews. So the visual effects supervi ...

When it comes to the development of robotaxis, we have graduated from a very difficult and technical science project to now a commercialization phase where we're increasingly going to be able to bring this technology to millions of people around the world. We think people should be excited about robo taxis, not just because of the future, but because they will be safer and make it more easy and convenient to get around your city. Robotaxi as a concept has been proven already.We have dozens of our cars roami ...

Vision AI agents are transforming semiconductor manufacturing operations, driving new levels of quality and productivity. At the DAI level, visual inspection agents identify defects more accurately by fine-tuning Vision Foundation models with labeled images and now millions of unlabeled images through self-supervised learning and NVIDIA TAU. At the wafer level, AI agents powered by NVIDIA Metropolis and Cosmos Reason identify defects and reason potential root causes.As manufacturing processes deviate over t ...

Heat. Heat. [music] Heat. Heat.[music]. ...

Open Model Impact - Open models democratize intelligence access, shifting focus to customer care and product excellence [1] - Open models transform users into makers, enabling specialization for specific use cases [2] Business Strategy - Success hinges on understanding and serving customers effectively, rather than resource dominance [1] - Customization of open models is key to creating unique value and applications [2]

AI Strategy & Implementation - Northrop Grumman utilizes an AI factory to enable both enterprise AI for internal efficiency and mission AI for product development and testing [1] - The company emphasizes the importance of having on-premise AI capabilities due to varying use cases, including those requiring private or classified environments [2] - Northrop Grumman has trained all of its over 100,000 employees on how to leverage AI, indicating a company-wide integration strategy [3] Operational Flexibility & Efficiency - AI tools enhance the company's existing toolkit, enabling private data usage, model leveraging, and training, providing increased flexibility [5] - AI accelerates workforce capabilities and facilitates rapid testing of new capabilities, offering engineers unprecedented flexibility [6] - The company can efficiently allocate workload to engineers without incurring significant costs in building unique environments, leveraging existing infrastructure [6] Regulatory Compliance - Northrop Grumman operates in a highly regulated industry, necessitating the ability to deploy AI tools and capabilities in various environments, including commercial and federal cloud environments, to meet employee needs [3][4]

AI Trends and Predictions - The AI industry is shifting towards specialization, with enterprises focusing on fine-tuning and specializing models for specific domains [6][8][82] - Open source technologies are driving transparency and adoption of AI agents, giving more power to enterprises and consumers [8][10] - The next wave of innovation is expected in world models, which are extremely data-intensive and will be the base layer for robotic opportunities [69][72] Challenges in AI Adoption - Agent memory is an unsolved problem, requiring agents to have persistent memory of both the user and itself [13][14][15] - Seamless communication between AI agents requires open source communication protocols [23][56] - AI security is crucial, with the potential need for a high ratio of security agents to cognitive intelligence agents [24][26] - Evaluating AI performance requires moving from academic benchmarks to real-world evaluations and reinforcement learning environments [34][38][39] Investment and Innovation - Capital investments are shifting from the model space to the agent space, driven by the focus on people and applications [58][59] - Enterprises seek AI solutions with high accuracy, small footprint, and data privacy [49][50] - Distillation, which involves making large models more efficient and smaller, is becoming important for cost-effectiveness [51][52] Enterprise Adoption Strategies - Enterprises should view model development as a software development platform, focusing on MVP and optimization over time [53][54][55] - Enterprises are adopting generative AI slower due to legacy systems and data locked in those systems [80][81] - Enterprises should focus on systems of smaller, specialized models rather than one model to solve all problems [83] Stochastic Mindset and Evaluation - AI compute is becoming more stochastic, requiring a shift in how we interface with and evaluate computers [30][32] - Verification of AI in specialized domains is challenging due to the difficulty and expense of expert verification [41] The Role of Open Source - Open source is critical for base models and communication protocols, enabling enterprises to build and compete with their own workflows [11][57] - A \$2 billion investment was raised with Nvidia's participation to support the US open source development ecosystem [11] Iteration and Mindset - Companies should iterate quickly, inspired by gradient descent algorithms, to gather information and find new opportunities [75][77] - Founders should pick a starting place that is exciting, big, and challenging enough to be worth the effort [79]

Industry Trend - AI 被认为是推动制造业回归美国并建立更智能工厂的关键驱动力 [1] - 行业希望建立 AI 原生工厂 [1]

Technology & Innovation - Nvidia is developing reasoning vision language action models (reasoning GLA) for vehicles [1] - Reasoning GLA integrates visual perception, language understanding, and action generation [1] - The model aims to mimic human-like step-by-step reasoning for decision-making in complex situations [1]

Quantum Technology Impact - Quantum mechanics 应用于产品将带来数量级的改进 [1] - 性能提升幅度巨大，远超 50% 或 100%，可达 10 倍、1 万倍甚至 1 百万倍 [1]