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.

Det här avsnittet är hämtat från ett öppet RSS-flöde och publiceras inte av Podme. Det kan innehålla reklam.

Avsnitt(792)

Why the Next AI Breakthrough May Come from Physics with Max Welling - #774

Why the Next AI Breakthrough May Come from Physics with Max Welling - #774

The conventional wisdom in AI is that the next breakthrough will come from more compute, more data, and larger models. But what if the next leap comes from somewhere else? In this episode, Max Wellin...

26 Aug 58min

Why Image Generation Needs More Than Bigger Models with Fatih Porikli - #773

Why Image Generation Needs More Than Bigger Models with Fatih Porikli - #773

Text-to-image models have become remarkably good at producing realistic images. But realism isn’t the same as correctness. Ask for several distinct people, a specific composition, or a high-resolution...

12 Aug 56min

Why Models Are AI’s Next Training Dataset with Damian Borth - #772

Why Models Are AI’s Next Training Dataset with Damian Borth - #772

For more than a decade, AI has advanced by training ever-larger models on ever-larger datasets. But as high-quality training data becomes harder to find and pretraining grows increasingly expensive, r...

27 Juli 47min

How AI Learns to Smell with Alex Wiltschko - #771

How AI Learns to Smell with Alex Wiltschko - #771

In this episode, Alex Wiltschko, founder and CEO of Osmo, joins the show to discuss his goal of giving computers a sense of smell and what it takes to build olfactory intelligence. We explore the sci...

8 Juli 59min

Why AI Agents Break the GenAI Security Model with Devvret Rishi - #770

Why AI Agents Break the GenAI Security Model with Devvret Rishi - #770

In this episode, Sam talks with Dev Rishi, GM of AI at Rubrik, about what happens when agents move beyond answering questions and start taking action across tools, systems, and business processes. We...

16 Juni 56min

Is RAG Dead? Lessons from Building AI for Tax Law with Alex Bowcut - #769

Is RAG Dead? Lessons from Building AI for Tax Law with Alex Bowcut - #769

As context windows grow into the millions of tokens, many AI practitioners are questioning whether retrieval-augmented generation (RAG) is still necessary. If modern models can ingest entire libraries...

9 Juni 51min

Relational Foundation Models for Enterprise Data with Jure Leskovec - #768

Relational Foundation Models for Enterprise Data with Jure Leskovec - #768

In this episode, Jure Leskovec, co-founder and chief scientist at Kumo and professor of computer science at Stanford, joins us to explore two fronts of his work: AI for science and relational deep lea...

21 Maj 1h 6min

How to Find the Agent Failures Your Evals Miss with Scott Clark - #767

How to Find the Agent Failures Your Evals Miss with Scott Clark - #767

In this episode, Scott Clark, co-founder and CEO of Distributional, joins us to explore how teams can reliably operate and improve complex LLM systems and agents in production. Scott introduces a Masl...

7 Maj 53min

Populärt inom Politik & nyheter

aftonbladet-krim
svenska-fall
p3-krim
en-runda-till
aftonbladet-daily
rss-krimstad
politiken
flashback-forever
rss-vad-fan-hande
rss-sanning-konsekvens
motiv
rss-krimreportrarna
svd-ledarredaktionen
rss-frandfors-horna
kungligt
spar
fordomspodden
rss-flodet
rss-expressen-dok
dagens-eko