This paper develops a method to compress a cell segmentation model called Cellpose-SAM to reduce its size, making it more suitable for deployment on low-power devices in laboratories. Practitioners who work with stem cell microscopy may care about this research because it helps ensure that compressed models still produce accurate results, which is crucial for applications where model accuracy is critical.
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Intelligence on the Edge: Liquid AI's Ramin Hasani on the Search for Device-Native Foundation Models
This episode features Ramin Hassani, CEO of Liquid AI, discussing the company's journey from biologically inspired neural networks at MIT to developing device-native foundation models. He makes a technically grounded case for efficient, har…
Device-native AIFoundation modelsNeural network architectureBiologically inspired AIOut-of-distribution generalizationComputational efficiencyNonlinear systemsHardware-aware AIAutomated model designGating mechanismsInput-dependent dynamicsEdge computingAI hardwareAgentic AIContinual learningEmergent intelligence