O B J E C T I V E  ///

AI EVOLVE explores the frontier of AI with advanced topics, focusing on Agentic AI, multi-agent systems, and a brief introduction to quantum-enhanced AI applications. 

^ Our educational framework for this course is presently in development. We will update this page when this product and service is available for purchase. Course Topics and Deliverables are subject to change until finalised.

C O U R S E    T O P I C S  ///

• • Advanced Agentic AI (AI Agents) and Autonomous Systems 

  • Multi-Agent Systems and Collaboration 

  • Advanced Natural Language Processing Techniques (BERT, GPT) 

  • Autonomous Decision-Making and Self-Learning Agents

  • Introduction to Quantum Computing Concepts for AI 

  • Quantum Machine Learning (QML) Overview  

  • Ethical and Regulatory Challenges in Advanced AI 

  • Hands-On Activity: Designing an Agentic AI Scenario or Exploring Quantum-AI Concepts 

            COURSE CODE :  PER-EVO-004

  COURSE DELIVERY :  ONLINE ONLY

COURSE DURATION : 1 DAY / 6 HRS

                     AVAILABLE :  TBA

W H A T    Y O U    W I L L    L E A R N  ///

In general, each course’s outcomes are crafted to ensure participants leave with a comprehensive understanding of the material, supported by hands-on experience and knowledge to apply concepts in real-world settings.

D E L I V E R A B L E S  ///

• • Advanced Agentic AI and Autonomous Systems

Outcome: Design simple autonomous systems, applying Agentic AI for self-directed actions and complex task automation.

• • Multi-Agent Systems and Collaboration 

Outcome: Understand the basics of multi-agent systems, preparing participants to create environments where agents interact.

• • Advanced Natural Language Processing Techniques (BERT, GPT) 

Outcome: Gain familiarity with state-of-the-art NLP models, understanding their use for advanced language processing tasks.

• • Autonomous Decision-Making and Self-Learning Agents

Outcome: Learn how AI agents can autonomously adapt to environments, understanding reinforcement learning basics.

• • Introduction to Quantum Computing Concepts for AI 

Outcome: Grasp foundational quantum computing principles and explore their theoretical applications in AI.

• • Quantum Machine Learning (QML) Overview  

Outcome: Recognize how quantum computing can potentially enhance AI model performance, preparing for future developments.

• • Ethical and Regulatory Challenges in Advanced AI 

Outcome: Understand the challenges of implementing advanced AI systems responsibly within regulatory boundaries.

• • Hands-On Activity: Designing an Agentic AI Scenario or Exploring Quantum-AI Concepts

 Outcome: Design an Agentic AI scenario or experiment with quantum-AI concepts, applying course topics practically.