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Exploring Memory Control in Models
Exploring the trade-off between efficiency and performance in models through control over memory, highlighting differences in compression and scalability with the number of tokens in models like transformers.
Nathan hosts Albert Gu, assistant professor at CMU and co-founder of Cartesia AI, to discuss the groundbreaking Mamba architecture. In this episode of The Cognitive Revolution, we explore the state space model revolution, diving into the technical details of Mamba and Mamba 2. Join us for an insightful conversation on the future of AI architectures and their potential to transform the field.
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Patrick McKenzie (@patio11) talks to experts who understand the complicated but not unknowable systems we rely on. You might be surprised at how quickly Patrick and his guests can put you in the top 1% of understanding for stock trading, tech hiring, and more.
Spotify: https://open.spotify.com/show/3Mos4VE3figVXleHDqfXOH
Apple: https://podcasts.apple.com/id1753399812https://podcasts.apple.com/id1753399812
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(00:00:00) About the Show
(00:05:39) State Space Models
(00:13:05) Intuition and inspiration
(00:18:27) Surprises
(00:22:33) Sponsors: Oracle | Brave
(00:24:41) Biological inspiration
(00:25:19) MAMBA breakthrough
(00:30:59) How does the state work?
(00:36:44) What is the size of the state?
(00:39:05) Training vs. Inference (Part 1)
(00:42:04) Sponsors: Omneky | Squad
(00:43:51) Training vs. Inference (Part 2)
(00:43:51) Sequence Models
(00:49:20) Mamba inference
(00:57:53) Mamba2 vs Mamba1
(01:16:05) Overtraining and the future of SSMs
(01:17:44) Training efficiency vs inference efficiency
(01:20:52) Hybrid models
(01:25:04) Scaling Attention Layers
(01:30:23) Optimizing State
(01:34:09) The extrapolation abilities of the SSMs
(01:36:37) Sequence parallelism with Mamba 2
(01:39:20) Why are you publishing all this?
(01:40:46) Cartesia and Together
(01:41:54) Outro
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