1Fundamentals of generative AI

• 1.1How AI, machine learning, and generative AI relate

  Understand the nesting of artificial intelligence (AI), machine learning (ML), deep learning, and generative AI; the learning types (supervised, unsupervised, reinforcement); structured vs unstructured data; and modalities such as text, image, audio, and multimodal—from a business perspective.

• 1.2Large language models and foundation models

  Understand the idea and generality of foundation models, how large language models (LLMs) predict the next token using tokens and prompts, the difference between pretraining and fine-tuning, terms like parameters and context window, and the basics of embeddings—from a business perspective.

• 1.3Limitations and risks of generative AI

  Learn the limitations and risks to understand when using generative AI in business: hallucinations (plausible but wrong outputs), bias from training data, knowledge cutoff, output uncertainty and reproducibility, the difficulty of explainability, and data privacy, copyright, and security.