Dynamic Computation and Linear Scaling in Transformers

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The idea of dynamic computation in transformers allows for processing each token differently based on context, unlike state space models that processed every input the same way. This selective mechanism enables an additional path for input to influence computation. Transformers have a more attention-like approach with dynamic computation, leading to linear scaling where each token prediction is constant time due to the state not growing over time. Theoretical and practical evidence suggests that this approach is effective and efficient.

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

RECOMMENDED PODCAST: Autopilot explores the adoption and rollout of AI in the industries that drive the economy and the dynamic founders bringing rapid change to slow-moving industries. From law, to hardware, to aviation, Will Summerlin interviews founders backed by Benchmark, Greylock, and more to learn how they're automating at the frontiers in entrenched industries.

Listen on Spotify: https://open.spotify.com/show/6YQZkKHN7EP2yWedAvSxBC?si=18377c69a2804333

Listen on Apple: https://podcasts.apple.com/ca/podcast/autopilot-with-will-summerlin/id1738163836

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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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