Selective Mechanism for Long-Lived Memory in Transformers

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A selective mechanism in encoding allows for a learned aspect in creating long-lived memories, particularly demonstrated on DNA in the original paper. This mechanism enables handling super long sequences, offering an alternative to the episodic nature of transformers where managing context windows is a challenge. The potential to build up, save, and fork states over time presents interesting opportunities. Contrary to earlier assumptions, the A100 utilized for training has more SRAM capacity, aiding in keeping states closer to computational processes.

In this first part of a two episode series, Nathan and AI scout Jason Meaux provide a sweeping overview of the first 90 days of Mamba-inspired research. They discuss the mechanistic underpinnings of Mamba architecture, Mamba's context capabilities, multi-modal applications in image segmentation and computer vision tasks, and much more. Try the Brave search API for free for up to 2000 queries per month at https://brave.com/api

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Show Notes and Paper Links: https://docs.google.com/document/d/1NK_a3deVL_aczORmSRw8LyujNPotpO7Kd90sIRj9Qx0/edit?usp=sharing

Nathan's original Mamba Deep Dive

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