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Chaindesk

"Research agent 3.0 - Build a group of AI researchers" - Here is how

Summary

During the weekend, the speaker demonstrated creating a research group with GPTs to enhance collaboration in extracting data from an Airtable list. The evolution of AI researchers was discussed, focusing on the development of higher-quality research agents like AI Research Agent 2.0, capable of complex tasks. Multi-agent systems like MGB and Chad def were leveraged for improved task performance and collaboration, leading to the concept of AI Researcher 3.0 with a research manager and specialized agents for quality control. The use of platforms like Grading AI for training specialized agents and Autogen for simplifying collaboration showcased efficient teamwork and customized multi-functionality for tasks like Google search and quality assurance. The integration of feedback loops, quality control procedures, and the Airtable API further enhanced research quality and efficiency in the multi-agent system.

Introduction to Research Group with GPTs

During the weekend, the speaker built a research group with GPTs to extract and share data from an Airtable list for collaborative research tasks.

Evolution of AI Researchers

The speaker discusses the evolution of AI researchers over the past 6 months, emphasizing the development of higher-quality research agents.

Development of AI Researcher 2.0

The speaker explains the creation of AI Research Agent 2.0, focusing on AI agents becoming goal-oriented and able to handle complex tasks.

Challenges and Advancements in Multi-Agent Systems

The challenges and advancements in multi-agent systems like MGB and Chad def are discussed, allowing for improved task performance and collaboration.

Creation of AI Researcher 3.0

The speaker introduces the concept of AI Researcher 3.0, incorporating a research manager for quality control and introducing more agents for specialized tasks.

Training Specialized Agents with Fine-Tuning and Knowledge Bases

The speaker discusses training specialized agents through fine-tuning and knowledge bases, highlighting the use of platforms like Grading AI for simplified fine-tuning processes.

Building a Multi-Agent Research System Step by Step

A detailed explanation of building a multi-agent research system step by step using GPT assistants for different roles like research manager, director, and researcher.

Implementation of Autogen for Assistants Collaboration

The implementation of Autogen for simplifying the collaboration between different agents and creating hierarchy and structure for efficient teamwork.

Utilizing AutoGen for Assistant API Integration

A demonstration of using AutoGen for Assistant API integration and creating a collaborative environment for assistants like researchers, managers, and directors.

Customizing Multi-Functionality for Improved Efficiency

The customization of multi-functionality for improved efficiency in tasks like Google search, web scripting, and quality assurance in research projects.

Enhancing Research Quality and Efficiency with Feedback Loops

The enhancement of research quality and efficiency through feedback loops and quality control procedures within the multi-agent research system.

Integration of Airtable API for Data Management

The integration of Airtable API for efficient data management in research tasks, including reading existing data and updating records for improved collaboration.

Optimizing Agent Memory and Collaborative Workflow

Strategies for optimizing agent memory and controlling collaborative workflows for better research outcomes in the AI researcher system.

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