Extending AI architectures to address continuous scientific problems
Artificial intelligence is proving to be transformative in its ability to work with language and images. Now, with a growing push to apply AI to scientific discovery, Caltech’s Anima Anandkumar says there is a crucial ingredient missing from most AI models: the ability to understand the physical world. Take, for example, weather models, says Anandkumar, Caltech’s Bren Professor of Computing and Mathematical Sciences. If you want an AI model to predict weather, it must understand chaotic physical systems, like how the atmosphere changes around the planet and over time.
Caltech scientists have built a record-breaking array of 6,100 neutral-atom qubits, a critical step toward powerful error-corrected quantum computers. The qubits maintained long-lasting superposition and exceptional accuracy, even while being moved within the array. This balance of scale and stability points toward the next milestone: linking qubits through entanglement to…
The climate is changing. This makes predicting the path of extreme-weather events, among other challenges, all the more difficult. AI powered by NVIDIA technology can help tackle such challenges in climate science and increase the accuracy of weather prediction. Dive deeper into AI for climate science at NVIDIA GTC, a…
After three years of uncertainty caused by the pandemic and its post-lockdown hangover, enterprises in 2023 — even with recession looming and uncertainty abounding — face the same imperatives as before: lead, innovate and problem solve. AI is becoming the common thread in accomplishing these goals. On average, 54% of…