Meta's NEW LLM Architecture is a GAME-CHANGER! LCMs vs LLMs

Meta's NEW LLM Architecture is a GAME-CHANGER! LCMs vs LLMs
Meta’s Large Concept Models (LCMs) are here to change the way we think about AI. Unlike traditional Large Language Models (LLMs) that rely on token-based processing, LCMs work with entire concepts-abstract ideas and actions. This shift could overcome many of the challenges LLMs face, like reasoning limitations and tokenization constraints. In this video, we explore how LCMs work, their three-layer architecture, and how they outperform LLMs in tasks like summary expansion and multilingual generalization. Are LCMs the future of AI? Watch to find out.
Topics Covered:
What are Large Concept Models (LCMs)?
How LCMs differ from traditional LLMs
LCM’s three-layer architecture: Encoder, Model, Decoder
Tasks like summary expansion and multilingual generalization
Why tokenization might soon be obsolete
Potential applications across text, speech, and multimodal AI
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[@CloudDataScience]

How do you think LCMs will change the landscape of GenAI?

[@dandushi9872]

What capabilities will an LCM have over an LLM? I understand that it can understand whole sentences but what are the benefits?

[@YouuRayy]

transformers already conceptualize internally. question is whether the number of token level lanes is also optimal for higher concept levels.

[@hoang4231]

can you made video more detail on V-JEPA in tearing paper into two pieces video 6:23 , like how it process layer to layer from begin to the end.

[@CloudDataScience]

Yeah great idea! Working on it!

[@CloudDataScience]

Link to Meta's paper: ai.meta.com/research/publications/large-concept-models-language-modeling-in-a-sentence-representation-space/