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What is NVIDIA Cosmos?
NVIDIA Cosmos is an innovative platform designed specifically for developers, featuring state-of-the-art generative World Foundation Models (WFMs), sophisticated video tokenizers, robust safety measures, and an efficient data processing and curation system that enhances the development of physical AI technologies. This platform equips developers engaged in fields like autonomous vehicles, robotics, and video analytics AI agents with the tools needed to generate highly realistic, physics-informed synthetic video data, drawing from a vast dataset that includes 20 million hours of both real and simulated footage. As a result, it allows for the quick simulation of future scenarios, the training of world models, and the customization of particular behaviors. The architecture of the platform consists of three main types of WFMs: Cosmos Predict, capable of generating up to 30 seconds of continuous video from diverse input modalities; Cosmos Transfer, which adapts simulations to function effectively across varying environments and lighting conditions, enhancing domain augmentation; and Cosmos Reason, a vision-language model that applies structured reasoning to interpret spatial-temporal data for effective planning and decision-making. Through these advanced capabilities, NVIDIA Cosmos not only accelerates the innovation cycle in physical AI applications but also promotes significant advancements across a wide range of industries, ultimately contributing to the evolution of intelligent technologies.
What is Core ML?
Core ML makes use of a machine learning algorithm tailored to a specific dataset to create a predictive model. This model facilitates predictions based on new incoming data, offering solutions for tasks that would be difficult or unfeasible to program by hand. For example, you could create a model that classifies images or detects specific objects within those images by analyzing their pixel data directly. After the model is developed, it is crucial to integrate it into your application and ensure it can be deployed on users' devices. Your application takes advantage of Core ML APIs and user data to enable predictions while also allowing for the model to be refined or retrained as needed. You can build and train your model using the Create ML application included with Xcode, which formats the models for Core ML, thus facilitating smooth integration into your app. Alternatively, other machine learning libraries can be utilized, and Core ML Tools can be employed to convert these models into the appropriate format for Core ML. Once the model is successfully deployed on a user's device, Core ML supports on-device retraining or fine-tuning, which improves its accuracy and overall performance. This capability not only enhances the model based on real-world feedback but also ensures that it remains relevant and effective in various applications over time. Continuous updates and adjustments can lead to significant advancements in the model's functionality.
API Availability
API Availability
Has API
Pricing Information
Free
Free Version
Pricing Information
Pricing not provided
Supported Platforms
SaaS
Windows
Linux
Supported Platforms
iPhone
iPad
Mac
Customer Service / Support
Standard Support
Web-Based Support
Customer Service / Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Company Facts
Organization Name
NVIDIA
Date Founded
1993
Company Location
United States
Company Website
www.nvidia.com/en-us/ai/cosmos/
Company Facts
Organization Name
Apple
Company Location
United States
Company Website
developer.apple.com/documentation/coreml
Categories and Features
Categories and Features
Development Frameworks
Not specified
Machine Learning
Not specified