Autonomous Multi Agent AI Systems

//Abstract In this presentation, we will explore how intelligent autonomous multi-agent systems can augment workflows. By leveraging collaborative multi-agent AI systems, people can automate routine tasks and streamline complex processes. We will go over the architecture of building multi-agent systems, talk about how to coordinate teams of AI agents that work together, discuss how to monitor and optimize these systems to be intelligent, and showcase real-world applications that highlight their potential to enhance efficiency. Link to Presentation: https://github.com/nv78/Autonomous-Intelligence/blob/main/materials/AutonomousIntelligence.pdf //Bio Natan has experience working as a Data Scientist / Software Engineer within Deloitte's Applied Artificial Intelligence division. At Deloitte, Natan collaborated on many AI projects in the domains of Natural Language Processing, Computer Vision and Big Data Analytics. He wrote the Deloitte Prompt Engineering Guide, and led execution for Ready AI, enabling clients to practically go from zero to one on their AI journeys. Natan loves building things. He has spent over 10,000 hours building AI projects such as AI fantasy soccer optimization models, reinforcement learning systems for robots, autonomous trash picking robots, AI generated music, music recommender systems, NLP solutions for document classification, avionic software systems on rockets, and federated learning products for medical images. Natan graduated from Cornell University with a Bachelors of Science in Electrical and Computer Engineering, and a Masters of Engineering in Computer Science. ------------------------------------------------------------------------------------------------------------------------- This is a bi-weekly "Agent Hour" event to continue the conversation about AI agents. Sponsored by Arcade Ai (https://www.arcade-ai.com/)