The chapter explores the journey of state-space models in AI, examining the transition from recurrent neural networks to models like Mamba, emphasizing the significance of stateful recurrence in sequencing and intelligence modeling. It discusses the computational challenges, inductive biases, and effectiveness of these models in handling complex modalities like audio and video. Additionally, the chapter delves into the process of blending theory and experimentation in scientific research, showcasing the importance of intuition and conviction in driving breakthrough discoveries.

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