This paper explores how AI can be applied across different stages of game development, from playing games to designing and testing them, and how to reuse capabilities across these stages. Practitioners might care about how to apply AI to improve game development efficiency and effectiveness.
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This paper proposes a new type of AI model that can create new knowledge and solutions on its own, rather than just solving problems that are already defined. Practitioners might care because this could enable AI systems to learn and improve in a more human-like way.
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…