Introduction to AI via use cases

This beginner-level course provides a comprehensive introduction to AI through real-world use cases. Participants will learn about the evolution of AI, the distinction between reasoning and learning systems, process automation, and generative AI.

The course combines foundational concepts with practical examples, ensuring participants gain both conceptual clarity and hands-on appreciation of AI’s potential and risks. Over three weeks (6 hours total), the course will progressively explore AI’s types, applications, ethical challenges, and future directions.

This training will cover the following topics:

1. Foundations and Forms of AI

• What is AI? Core definitions and perspectives
• A brief history of AI: from symbolic reasoning to modern machine learning
• Different types of AI: reasoning systems, learning systems, automation, and agents
• Pros and Cons of each, ethical constraints

• Use cases across industries: healthcare, finance, logistics, customer service

2. Applications and Use Cases

• Process automation
• Intention-based ML and predictive models
• Practical exploration of AI in different domains (decision support, risk detection, personalization)

• Risks, limitations, and biases of AI systems

3. Generative AI and LLMs

• What are LLMs and how they work (conceptual overview)
• Applications of Generative AI in text, images, and beyond
• Opportunities and limitations of LLMs
• Ethical considerations: privacy, transparency, responsible use

• Future outlook: AI agents, reasoning models, and the road to AGI

By the end of the training, participants will be able to:

• Explain what AI is and describe its historical development
• Differentiate between reasoning systems, learning systems, and automation approaches and understand their respective potential and limitations
• Identify how AI is used in various industries and discuss its risks and benefits

• Recognize ethical and safe usage practices for AI technologies
• Engage confidently in conversations about AI in professional and personal contexts

• Short lectures with real-world case studies
• Interactive discussions and group exercises
• Demonstrations of AI tools (including LLMs)

• Hands-on “mini workout” to test AI applications