Core Viewpoint - NVIDIA collaborates with Novo Nordisk to enhance drug discovery through advanced AI technologies, utilizing the Gefion supercomputer for innovative research and development applications [1][3][13] Group 1: Collaboration and Technology - The partnership aims to create customized AI models for early research and clinical development, leveraging advanced simulation and physical AI technologies [2] - Gefion, powered by NVIDIA DGX SuperPOD™, serves as an AI factory for Novo Nordisk, enabling the execution of drug discovery and agentic AI workloads [3][5] - NVIDIA's tools, including BioNeMo™, NIM™, and NeMo™, will facilitate generative AI-powered drug discovery and the development of customized workflows [3] Group 2: Research Focus - Novo Nordisk researchers will utilize single-cell models to predict cellular responses to drug candidates and design models for drug-like molecules [4] - The collaboration will also focus on building biomedical large language models from Novo Nordisk's extensive scientific literature to identify correlations between genes, proteins, and diseases [4] Group 3: Impact on Healthcare - Gefion's computational power is expected to address significant R&D challenges, aiming to unlock new possibilities in pharmaceutical research and development [6] - Danish startup Teton is using Gefion to develop an AI care companion for hospitals, which has shown a 25% reduction in nightshift duties for nurses [7] - Gefion will support efforts to unify health data across Danish health organizations, facilitating secure access to interconnected health data for research [9]

Core Insights - NVIDIA has announced the release of new Cosmos world foundation models (WFMs) aimed at enhancing physical AI development, providing developers with customizable reasoning models for world generation [1][3][21] - The introduction of two new blueprints powered by NVIDIA Omniverse and Cosmos platforms will facilitate large-scale synthetic data generation for robots and autonomous vehicles, with early adopters including industry leaders like 1X and Uber [2][21] Group 1: Cosmos World Foundation Models - Cosmos WFMs enable the generation of controllable photorealistic video outputs from structured video inputs, streamlining perception AI training [3][4] - The models are designed to enhance robotics and physical industries, allowing for significant advancements in these fields [3][21] - Cosmos Predict WFMs can generate virtual world states from multimodal inputs, enabling multi-frame generation and customized training for physical AI applications [7][8] Group 2: Synthetic Data Generation - The Cosmos Transfer model allows for the transformation of 3D simulations into photorealistic videos, significantly improving the efficiency of synthetic data generation [4][6] - Companies like Agility Robotics and Foretellix are leveraging these models to create diverse datasets for training their robotic and autonomous systems [5][8] - The GR00T Blueprint combines Omniverse and Cosmos Transfer to reduce data collection time from days to hours, enhancing the efficiency of synthetic manipulation motion generation [6] Group 3: Multimodal Reasoning and Data Curation - Cosmos Reason is a customizable model that utilizes chain-of-thought reasoning to interpret video data and predict interaction outcomes, improving data annotation and curation for physical AI [9][10] - Developers can utilize NVIDIA's NeMo framework for accelerated data processing and curation, with applications in training large vision language models [11][12] - Companies like Linker Vision and Milestone Systems are employing these tools for video data curation to enhance their AI capabilities [12] Group 4: Responsible AI and Availability - NVIDIA emphasizes responsible AI practices by implementing open guardrails across all Cosmos WFMs and collaborating with Google DeepMind to watermark AI-generated outputs [13] - The Cosmos WFMs are available for preview in the NVIDIA API catalog and listed in the Vertex AI Model Garden on Google Cloud, with some models accessible on platforms like Hugging Face and GitHub [14]