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MAMBA and SSMs Explained - AI Coffee Break with Letitia

aicoffeebreakwl.substack.com · 2,770 words · saved by 1 readers

In the ever-evolving world of AI, there's a new contender making waves—MAMBA. It's been generating quite a buzz, and for good reason. Some even say it could replace the ubiquitous Transformer. Surprisingly, the MAMBA paper was initially rejected at ICLR, but this hasn't stopped it from gaining traction in the AI community. In this post, we'll dive deep into what makes MAMBA so exciting and why it's being hailed as a game-changer. To fully understand MAMBA, we first need to explore the concept of State Space Models (SSMs), which form the foundation of this new architecture. So, grab a cup of coffee and let's break it down. « Optional: Enjoy this post in video format 👇! » MAMBA, a groundbreaking architecture introduced by Albert Gu and Tri Dao (the latter name you might recognize from his work on Flash Attention 1, 2, and 3—has quickly captured the attention of the AI community. Its main innovation is the enhancement of State Space Models (SSMs), which were already faster and more memor

In the ever-evolving world of AI, there's a new contender making waves—MAMBA. It's been generating quite a buzz, and for good reason. Some even say it could replace the ubiquitous Transformer. Surprisingly, the MAMBA paper was initially rejected at ICLR, but this hasn't stopped it from gaining traction in the AI community. In this post, we'll dive deep into what makes MAMBA so exciting and why it's being hailed as a game-changer. To fully understand MAMBA, we first need to explore the concept of State Space Models (SSMs), which form the foundation of this new architecture. So, grab a cup of…

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