Decoding Mamba: The Next Big Leap in AI Sequence Modeling

Hello everyone, and welcome to today’s deep dive into a fascinating paper titled “Mamba: Linear Time Sequence Modeling with Selective State Spaces” by Albert Gu and Tri Dao.

Mamba has been creating waves in the AI community, touted as a potential rival to the famed Transformers. Its claim to fame lies in its ability to scale impressively to lengthy sequences. But what really sets Mamba apart in the crowded landscape of sequence modeling?

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To understand Mamba’s place, let’s briefly revisit the existing models

1. Transformers: Known for their attention mechanisms where any part of a sequence can dynamically interact with any other, transformers, especially with causal attention, are adept at handling individual elements of a sequence. However, they come with a significant computational and memory cost, scaling with the square of the sequence length (L²).
2. Recurrent Neural Networks (RNNs)…