AIDU-AI-101
Delivery Type: Live, instructor-led Remote or in-person
Prerequisite: No technical, mathematical, or programming background required
This course provides a rigorous, non-technical foundation for understanding how modern AI systems behave in real organizational settings. Rather than focusing on tools, coding, or mathematics, it builds durable mental models for how AI systems are structured, how they differ from human intelligence, and why fluent outputs can be misleading.
Participants learn the core paradigms that underpin modern AI, including agents, learning systems, search, and planning, and how these paradigms appear in real workflows such as decision support, automation, and operations. AI is treated as a socio-technical system, shaped by data, objectives, incentives, and human interaction, not as a standalone technical artifact.
The course emphasizes common failure modes, over-trust, safety risks, and the gap between impressive demonstrations and real-world performance. By the end, participants are equipped to critically assess AI systems, vendor claims, and proposed use cases, and to make informed decisions about adoption, oversight, and governance.
Core Topics:
Foundational AI paradigms, agents, learning systems, planners
Search mechanisms and optimization in decision making
Learning mechanisms, machine learning and generalization
Planning mechanisms and structured decision making
Representational limits of AI systems
System limitations and real-world failure modes
Human-in-the-loop architectures and oversight
Illusions of intelligence and over-trust in AI outputs
AI safety foundations and unintended consequences
Evaluation and validation beyond demos and benchmarks
Value creation versus speculation
Non-automatable, judgment-heavy domains
Outcomes:
Explain how AI systems are structured and how they differ from human intelligence
Distinguish between major AI paradigms and their real-world roles
Understand why AI outputs appear intelligent without understanding
Identify common AI failure modes and safety risks
Critically evaluate AI products, vendor claims, and demonstrations
Recognize where AI should and should not be used
Make informed decisions about AI adoption, oversight, and governance
