This paper proposes a runtime system called REA that enables the coordination of fast, reactive policies (like those used in vision-language models) with slower, deliberative brain models, allowing them to work together effectively. Practitioners might care about REA because it can improve the performance and efficiency of embodied systems like robots.
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This paper shows that foundation models can be trained with a single algorithm, which can improve the accuracy-compute tradeoff and task coverage. Practitioners might care because this approach can simplify and streamline the training process for foundation models.
This paper introduces a new AI model called Venus-DefakerOne2 that can detect a wide range of fake images, videos, and documents, and is useful for developers who need to build robust anti-forgery systems.
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
Professor Michael I. Jordan argues that current AI discourse, focused on AGI and superintelligence, is a harmful distraction for young researchers and lacks economic thinking. He advocates for a 'collectivist economic perspective' on AI, vi…