Mamba, Mamba-2 and Post-Transformer Architectures for Generative AI with Albert Gu - #693
Today, we're joined by Albert Gu, assistant professor at Carnegie Mellon University, to discuss his research on post-transformer architectures for multi-modal foundation models, with a focus on state-space models in general and Albert’s recent Mamba and Mamba-2 papers in particular. We dig into the efficiency of the attention mechanism and its limitations in handling high-resolution perceptual modalities, and the strengths and weaknesses of transformer architectures relative to alternatives for various tasks. We dig into the role of tokenization and patching in transformer pipelines, emphasizing how abstraction and semantic relationships between tokens underpin the model's effectiveness, and explore how this relates to the debate between handcrafted pipelines versus end-to-end architectures in machine learning. Additionally, we touch on the evolving landscape of hybrid models which incorporate elements of attention and state, the significance of state update mechanisms in model adaptability and learning efficiency, and the contribution and adoption of state-space models like Mamba and Mamba-2 in academia and industry. Lastly, Albert shares his vision for advancing foundation models across diverse modalities and applications.
The complete show notes for this episode can be found at https://twimlai.com/go/693.
The State Space Model Revolution, with Albert Gu
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.
Apply to join over 400 founders and execs in the Turpentine Network: https://hmplogxqz0y.typeform.com/to/JCkphVqj
RECOMMENDED PODCAST:
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
SPONSORS:
Oracle Cloud Infrastructure (OCI) is a single platform for your infrastructure, database, application development, and AI needs. OCI has four to eight times the bandwidth of other clouds; offers one consistent price, and nobody does data better than Oracle. If you want to do more and spend less, take a free test drive of OCI at https://oracle.com/cognitive
The Brave search API can be used to assemble a data set to train your AI models and help with retrieval augmentation at the time of inference. All while remaining affordable with developer first pricing, integrating the Brave search API into your workflow translates to more ethical data sourcing and more human representative data sets. Try the Brave search API for free for up to 2000 queries per month at https://bit.ly/BraveTCR
Omneky is an omnichannel creative generation platform that lets you launch hundreds of thousands of ad iterations that actually work customized across all platforms, with a click of a button. Omneky combines generative AI and real-time advertising data. Mention "Cog Rev" for 10% off https://www.omneky.com/
Head to Squad to access global engineering without the headache and at a fraction of the cost: head to https://choosesquad.com/ and mention “Turpentine” to skip the waitlist.
CHAPTERS:
(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
State Space Models and Real-time Intelligence with Karan Goel and Albert Gu from Cartesia
This week on No Priors, Sarah Guo and Elad Gil sit down with Karan Goel and Albert Gu from Cartesia. Karan and Albert first met as Stanford AI Lab PhDs, where their lab invented Space Models or SSMs, a fundamental new primitive for training large-scale foundation models. In 2023, they Founded Cartesia to build real-time intelligence for every device. One year later, Cartesia released Sonic which generates high quality and lifelike speech with a model latency of 135ms—the fastest for a model of this class.
Sign up for new podcasts every week. Email feedback to show@no-priors.com
Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @krandiash | @_albertgu
Show Notes:
(0:00) Introduction
(0:28) Use Cases for Cartesia and Sonic
(1:32) Karan Goel & Albert Gu’s professional backgrounds
(5:06) State Space Models (SSMs) versus Transformer Based Architectures
(11:51) Domain Applications for Hybrid Approaches
(13:10) Text to Speech and Voice
(17:29) Data, Size of Models and Efficiency
(20:34) Recent Launch of Text to Speech Product
(25:01) Multimodality & Building Blocks
(25:54) What’s Next at Cartesia?
(28:28) Latency in Text to Speech
(29:30) Choosing Research Problems Based on Aesthetic
(31:23) Product Demo
(32:48) Cartesia Team & Hiring