Mamba, Mamba-2 and Post-Transformer Architectures for Generative AI with Albert Gu - #693

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.

Jaksot(778)

Teaching Large Language Models to Reason with Reinforcement Learning with Alex Havrilla - #680

Teaching Large Language Models to Reason with Reinforcement Learning with Alex Havrilla - #680

Today we're joined by Alex Havrilla, a PhD student at Georgia Tech, to discuss "Teaching Large Language Models to Reason with Reinforcement Learning." Alex discusses the role of creativity and explora...

16 Huhti 202446min

Localizing and Editing Knowledge in LLMs with Peter Hase - #679

Localizing and Editing Knowledge in LLMs with Peter Hase - #679

Today we're joined by Peter Hase, a fifth-year PhD student at the University of North Carolina NLP lab. We discuss "scalable oversight", and the importance of developing a deeper understanding of how ...

8 Huhti 202449min

Coercing LLMs to Do and Reveal (Almost) Anything with Jonas Geiping - #678

Coercing LLMs to Do and Reveal (Almost) Anything with Jonas Geiping - #678

Today we're joined by Jonas Geiping, a research group leader at the ELLIS Institute, to explore his paper: "Coercing LLMs to Do and Reveal (Almost) Anything". Jonas explains how neural networks can be...

1 Huhti 202448min

V-JEPA, AI Reasoning from a Non-Generative Architecture with Mido Assran - #677

V-JEPA, AI Reasoning from a Non-Generative Architecture with Mido Assran - #677

Today we’re joined by Mido Assran, a research scientist at Meta’s Fundamental AI Research (FAIR). In this conversation, we discuss V-JEPA, a new model being billed as “the next step in Yann LeCun's vi...

25 Maalis 202447min

Video as a Universal Interface for AI Reasoning with Sherry Yang - #676

Video as a Universal Interface for AI Reasoning with Sherry Yang - #676

Today we’re joined by Sherry Yang, senior research scientist at Google DeepMind and a PhD student at UC Berkeley. In this interview, we discuss her new paper, "Video as the New Language for Real-World...

18 Maalis 202449min

Assessing the Risks of Open AI Models with Sayash Kapoor - #675

Assessing the Risks of Open AI Models with Sayash Kapoor - #675

Today we’re joined by Sayash Kapoor, a Ph.D. student in the Department of Computer Science at Princeton University. Sayash walks us through his paper: "On the Societal Impact of Open Foundation Models...

11 Maalis 202440min

OLMo: Everything You Need to Train an Open Source LLM with Akshita Bhagia - #674

OLMo: Everything You Need to Train an Open Source LLM with Akshita Bhagia - #674

Today we’re joined by Akshita Bhagia, a senior research engineer at the Allen Institute for AI. Akshita joins us to discuss OLMo, a new open source language model with 7 billion and 1 billion variants...

4 Maalis 202432min

Training Data Locality and Chain-of-Thought Reasoning in LLMs with Ben Prystawski - #673

Training Data Locality and Chain-of-Thought Reasoning in LLMs with Ben Prystawski - #673

Today we’re joined by Ben Prystawski, a PhD student in the Department of Psychology at Stanford University working at the intersection of cognitive science and machine learning. Our conversation cente...

26 Helmi 202425min

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