Mixture of Agents: Multi-Agent meets MoE?

Описание: Discover how MoA combines the collective strengths of multiple LLMs to set new quality benchmarks, building on innovations like the Mixture of Experts within transformer architectures. This session will delve into how MoA enhances standard multi-headed self-attention mechanisms, offering significant performance improvements. We'll dissect the original research, examine the structural foundations and assumptions, and provide a detailed performance analysis. Join us for a comprehensive walkthrough of the MoA concept and its practical implementation. Whether you're looking to enhance your AI toolkit or integrate MoA into your production environments, this event is your gateway to understanding and leveraging the full potential of multi-agent LLM applications

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Speakers:
​Dr. Greg, Co-Founder & CEO AI Makerspace
  / gregloughane  

The Wiz, Co-Founder & CTO AI Makerspace
  / csalexiuk  

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00:00:00 Understanding Mixture of Agents vs. Mixture of Experts
00:04:02 Exploring Existential Questions with AI
00:09:07 Understanding Self-Refinement in AI Models
00:13:00 Understanding Neural Network Layers
00:16:47 Understanding Large Language Models (LLMs) as Agents
00:20:26 Exploring Self-Reflective Neural Networks
00:24:11 Understanding the Mixture of Experts Approach
00:28:59 Introduction to the Event and Build of the Day
00:31:58 Understanding Multi-Agent Layer System
00:35:23 Challenges with LLM Context and Bias
00:38:50 Experimenting with Llama 3.1 Model and Aggregator Prompts
00:41:48 Layered Approach to AI Complexity
00:45:15 Understanding Multi-Agent Systems
00:49:10 Advantages of Using Smaller, Flexible Models
00:52:48 Future of MOA in AGI Development
00:56:24 Exploring the Critique and Actor-Critic Model
01:00:20 Conclusion and Feedback Request