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Given recent advances in computer vision, the popularity of convolution-free models has risen quickly, demonstrating state-of-the-art accuracy in various tasks, such as image classification, segmentation and generation. In light of these recent advances, vision transformers and multi-layer perceptrons (MLP) models can already achieve ~90% Top-1 accuracy on the standard benchmark ImageNet dataset.
Given recent advances in computer vision, the popularity of convolution-free models has risen quickly, demonstrating state-of-the-art accuracy in various tasks, such as image classification, segmentation and generation. In light of these recent advances, vision transformers and multi-layer perceptrons (MLP) models can already achieve ~90% Top-1 accuracy on the standard benchmark ImageNet dataset.
We recently caught up with Bill Fox, Healthcare and Life Sciences Lead at SambaNova Systems, to ask him to share his thoughts on what excites him about AI, the biggest challenges for organizations adopting AI, and the most interesting projects he’s worked on in his career.
The JADC2 sensor network is providing more intelligence than ever before. Processing this data and turning it into actionable information seems like a perfect fit for artificial intelligence and machine learning projects, but is available technology able to keep up with the flow of data? SambaNova’s public sector lead, Bill Minarchi, explores the state of current technology and what we can look forward to in next generation projects.
SambaNova Systems, a leader in the AI/ML space, provides vitally needed AI/ML-centric computing solutions for companies and organizations of all sizes.
Complex AI for science workloads—with increasingly large deep learning models—are limited by the constraints of traditional computing architectures.
Using the capabilities of SambaNova’s DataScale system, together, researchers at the U.S. Department of Energy’s Argonne National Laboratory and SambaNova have advanced state-of-the-art accuracy for an important neutrino physics image segmentation problem.