AI, ML and Generative AI - for Managers and Business Leaders

Explain how artificial intelligence, from early programs to Deep Blue's 1997 win, simulates human thinking in machines programmed to learn, with weather forecasts and personalized recommendations.

Understand supervised learning through labeled data, where input and output align as objects and labels. Distinguish classification and regression with examples like spam filters, dog images, and house price prediction.

Learn how dimensionality reduction, a type of unsupervised learning, trims away unnecessary features to reduce randomness, noise, and cost in data for applications like MRI, financial data, and customer segmentation.

Discover how overfitting makes a model excel on training data but falter in real life, driven by biased data and evaluation on a subset, and ways to address it.

Explore natural language processing, a branch of AI that enables machines to understand, interpret, and generate human language, moving from scripted IVR to flexible gen-AI content generation.

Tokens drive costs in llm usage, since input and output tokens count toward charges, with limits to save costs, and enterprise models ChatGPT and Gemini may process tokens.

Learn how the context window limits an LLM's memory by total input and output tokens, and compare model token capacities to choose the right one for tasks like summarization.

Learn how retrieval augmented generation uses external data sources from a vector database and semantic search to create modified prompts that deliver contextually relevant, latest AI outputs with reduced hallucination.

Explore role play prompting by asking the model to act as a role-specific expert, such as a historian or chef, to shape tone, format, and output quality.

Master meta prompting by using an llm to generate prompts, enabling prompt engineering where the model acts as a prompt designer for complex tasks like a 10-day italy itinerary.