AI STRATEGY
The Nvidia-powered NASA Pleiades Supercomputer operates with
217,088 CUDA cores
Jensen says:“ AI is the engine of modern science – and large, open models for America’ s researchers will ignite the next industrial revolution.“ In collaboration with NSF and Ai2, we’ re accelerating innovation with state-of-the-art infrastructure that empowers US scientists to generate limitless intelligence, making it America’ s most powerful and renewable resource.”
Looking beyond Earth to space-based computing The collaboration is now extending beyond terrestrial applications into space itself.
Companies like Hewlett Packard Enterprise( HPE), Nvidia, IBM and SpaceX, in collaboration with NASA and the US Department of Defense, are pioneering radiation-hardened servers, AI-driven automation and high-performance computing( HPC) solutions for space-based operations.
“ Orbital debris is a huge problem for NASA – AI will play a significant role in detection and remediation actions,” David forecasts.
“ NASA will balance innovation with prudent risk management as we adopt and implement more and more AI, especially when complementing the entire workforce with Gen AI tools.”
This is the next frontier: deploying AI computing infrastructure directly in space, enabling real-time processing of scientific data without the delays and bandwidth limitations of transmitting everything back to Earth.
From monitoring climate change through DeepSat to detecting gravitational waves with LIGO, from atmospheric chemistry simulation to space-based computing, the NASA-Nvidia partnership has consistently pushed the boundaries of what’ s possible in computational science.
Dr Huerta’ s observation captures the essence of this collaboration:“ Making real-time analysis possible is the key to realising multi-messenger astrophysics, one of the top ten big ideas for future investment for the US National Science Foundation.”
As we face challenges ranging from climate change to understanding the nature of the universe, the partnership between NASA and Nvidia demonstrates how AI strategy in science requires not just advanced algorithms, but the computational infrastructure, collaborative frameworks – and long-term vision to transform how we discover knowledge about our world and beyond. aimagazine. com 139