Evolving AI Architectures: From Transformers to State Space Models

This chapter explores the transition from traditional transformer models to state space models in AI, highlighting their computational efficiency and design implications. It discusses the potential for hybrid models and introduces novel approaches like CANS, emphasizing the evolving landscape of AI architectures and their future performance.

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In this special crossover episode of The Cognitive Revolution, Nathan Labenz joins Robert Wright of the Nonzero newsletter and podcast to explore pressing questions about AI development. They discuss the nature of understanding in large language models, multimodal AI systems, reasoning capabilities, and the potential for AI to accelerate scientific discovery. The conversation also covers AI interpretability, ethics, open-sourcing models, and the implications of US-China relations on AI development.

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