AI LCM stacks - terrytaylorbonn/auxdrone GitHub Wiki

What I am waiting for.. the next revolution.

What are large concept models

Large Concept Models (LCMs) represent a new approach to language modeling that shifts away from word-by-word prediction towards understanding and reasoning with higher-level concepts. Instead of focusing on individual words, LCMs process language at the level of entire sentences or coherent ideas, allowing for more abstract and human-like reasoning. Here's a more detailed explanation: Key Features of LCMs: Concept-based Processing: Unlike traditional language models (LLMs) that work with tokens (words), LCMs work with sentence embeddings that capture high-level semantic representations. Semantic Understanding: LCMs are designed to understand the meaning of sentences and their relationships, rather than simply predicting the next word. Hierarchical Reasoning: LCMs can reason about abstract ideas and their connections, potentially leading to more human-like understanding and problem-solving. Improved Generalization: By focusing on concepts, LCMs can generalize better to new languages and tasks, requiring less fine-tuning compared to LLMs. Longer Context Handling: LCMs can handle longer inputs more efficiently because they process concepts rather than individual words. Modular and Extensible: LCMs can be designed with modular components, making it easier to add new languages or modalities without retraining the entire model. How LCMs Differ from LLMs: Input: LCMs operate on sentence embeddings, while LLMs operate on individual tokens. Focus: LCMs focus on understanding and reasoning with concepts, while LLMs focus on predicting the next token. Reasoning: LCMs are designed for hierarchical reasoning, while LLMs typically rely on sequential prediction. Generalization: LCMs tend to generalize better to new languages and tasks, while LLMs often require extensive fine-tuning. In essence, LCMs represent a paradigm shift in AI, moving from a purely token-based approach to one that emphasizes conceptual understanding and reasoning. This shift could lead to AI systems that are more intuitive, adaptable, and human-like in their ability to understand and interact with the world.