More than 10,000 companies building on the NVIDIA Jetson platform can now use new generative AI, APIs and microservices to accelerate industrial digitalization.
Powerful generative AI models and cloud-native APIs and microservices are coming to the edge. Generative AI is bringing the power of transformer models and large language models to virtually every industry. That reach now includes areas that touch edge, robotics and logistics systems: defect detection, real-time asset tracking, autonomous planning and navigation, human-robot interactions and more.
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NVIDIA announced major expansions to two frameworks on the NVIDIA Jetson platform for edge AI and robotics: the NVIDIA Isaac ROS robotics framework has entered general availability, and the NVIDIA Metropolis expansion on Jetson is coming next.
To accelerate AI application development and deployments at the edge, NVIDIA has also created a Jetson Generative AI Lab for developers to use with the latest open-source generative AI models.
More than 1.2 million developers and over 10,000 customers have chosen NVIDIA AI and the Jetson platform, including Amazon Web Services, Cisco, John Deere, Medtronic, Pepsico and Siemens.
With the rapidly evolving AI landscape addressing increasingly complicated scenarios, developers are being challenged by longer development cycles to build AI applications for the edge. Reprogramming robots and AI systems on the fly to meet changing environments, manufacturing lines and automation needs of customers is time-consuming and requires expert skills.
Generative AI offers zero-shot learning — the ability for a model to recognize things specifically unseen before in training — with a natural language interface to simplify the development, deployment and management of AI at the edge.
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Transforming the AI Landscape
Generative AI dramatically improves ease of use by understanding human language prompts to make model changes. Those AI models are more flexible in detecting, segmenting, tracking, searching and even reprogramming — and help outperform traditional convolutional neural network-based models.
Generative AI is expected to add $10.5 billion in revenue for manufacturing operations worldwide by 2033, according to ABI Research.
“Generative AI will significantly accelerate deployments of AI at the edge with better generalization, ease of use and higher accuracy than previously possible,” said Deepu Talla, vice president of embedded and edge computing at NVIDIA. “This largest-ever software expansion of our Metropolis and Isaac frameworks on Jetson, combined with the power of transformer models and generative AI, addresses this need.”
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