The hosts look at utility functions as the mathematical basis for making AI systems. They use the example of a travel agent that doesn’t get tired and can be increased indefinitely to meet increasing customer demand. They also discuss the difference between this structured, economic-based approach with the problems of using large language models for multi-step tasks.

This episode is part 2 of our series about building smarter AI agents from the fundamentals. Listen to Part 1 about mechanism design HERE.

Show notes:

• Discussing the current AI landscape where companies are discovering implementation is harder than anticipated
• Introducing the travel agent use case requiring ingestion, reasoning, execution, and feedback capabilities
• Explaining why LLMs aren't designed for optimization tasks despite their conversational abilities
• Breaking down utility functions from economic theory as a way to quantify user preferences
• Exploring concepts like indifference curves and marginal rates of substitution for preference modeling
• Examining four cases of utility relationships: independent goods, substitutes, complements, and diminishing returns
• Highlighting how mathematical optimization provides explainability and guarantees that LLMs cannot
• Setting up for future episodes that will detail the technical implementation of utility-based agents

Subscribe so that you don't miss the next episode. In part 3, Andrew and Sid will explain linear programming and other optimization techniques to build upon these utility functions and create truly personalized travel experiences.

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