This Modern Artificial Intelligence course from IBM Technology provides a clear and practical overview of the latest AI concepts used in real-world systems and enterprise applications. It is designed for learners who want to understand how modern AI technologies are built and applied in industry.
The course begins by explaining the difference between predictive AI and generative AI, helping learners understand when and how each approach is used. It then introduces AI agents and the essential skills required to build intelligent autonomous systems.
Students will also explore important architectural concepts such as RAG (Retrieval-Augmented Generation), multimodal AI, and how large language models process different types of data including text and images.
The course covers advanced topics such as AI technical debt, which explains the risks and long-term challenges in machine learning projects. It also introduces human-in-the-loop (HITL) systems, showing how human feedback improves AI performance and reliability.
Additional topics include physical AI, where robots learn and adapt in real-world environments, and neuro-symbolic AI, which combines reasoning with neural networks for more intelligent decision-making systems.
Learners will also study AI agent communication protocols, multimodal RAG systems, and the growing ecosystem of AI tools and frameworks used in modern AI development.
By the end of this course, learners will have a strong understanding of advanced AI concepts, AI system design, and the technologies powering modern artificial intelligence applications in real-world environments.
