Training physical AI models used to power autonomous machines, such as robots and autonomous vehicles, requires huge amounts of data. Acquiring large sets of…
Training physical AI models used to power autonomous machines, such as robots and autonomous vehicles, requires huge amounts of data. Acquiring large sets of diverse training data can be difficult, time-consuming, and expensive. Data is often limited due to privacy restrictions or concerns, or simply may not exist for novel use cases. In addition, the available data may not apply to the full range…
NVIDIA announced new USD-based generative AI and NVIDIA-accelerated development tools built on NVIDIA Omniverse at SIGGRAPH 2024. These advancements will expand…
At SIGGRAPH 2024 this week, NVIDIA is showcasing the latest advancements in the NVIDIA Maxine AI developer platform, available through NVIDIA AI…
Traditional video analytics applications and their development workflow are typically built on fixed-function, limited models that are designed to detect and…
The ability to use simple APIs to integrate pretrained AI foundation models into products and experiences has significantly increased developer usage of LLM…
NVIDIA collaborated with Mistral to co-build the next-generation language model that achieves leading performance across benchmarks in its class. With a growing…
Over 300M computed tomography (CT) scans are performed globally, 85M in the US alone. Radiologists are looking for ways to speed up their workflow and generate…
Engineering simulation is used across industries to accelerate product development. Simulations are used to check the safety of aircraft, cars, and buildings,…
Recent advancements in generative AI and multi-view reconstruction have introduced new ways to rapidly generate 3D content. However, to be useful for downstream…