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Utility functions: Building smarter AI agents from the fundamentals, part 2

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Manage episode 488241816 series 3475282
Content provided by Dr. Andrew Clark & Sid Mangalik, Dr. Andrew Clark, and Sid Mangalik. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Dr. Andrew Clark & Sid Mangalik, Dr. Andrew Clark, and Sid Mangalik or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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.
What did you think? Let us know.

Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics:

  • LinkedIn - Episode summaries, shares of cited articles, and more.
  • YouTube - Was it something that we said? Good. Share your favorite quotes.
  • Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.
  continue reading

Chapters

1. Critical thinking: What we're reading (00:00:00)

2. Media hype vs AI reality (00:05:30)

3. What are utility functions? (00:09:45)

4. Why AI agents need utility functions (00:17:00)

5. Example: AI travel agent and decisions (00:27:00)

6. Linear programming: where to go from here (00:35:00)

7. Closing: Agents and governance (00:36:40)

33 episodes

Artwork
iconShare
 
Manage episode 488241816 series 3475282
Content provided by Dr. Andrew Clark & Sid Mangalik, Dr. Andrew Clark, and Sid Mangalik. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Dr. Andrew Clark & Sid Mangalik, Dr. Andrew Clark, and Sid Mangalik or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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.
What did you think? Let us know.

Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics:

  • LinkedIn - Episode summaries, shares of cited articles, and more.
  • YouTube - Was it something that we said? Good. Share your favorite quotes.
  • Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.
  continue reading

Chapters

1. Critical thinking: What we're reading (00:00:00)

2. Media hype vs AI reality (00:05:30)

3. What are utility functions? (00:09:45)

4. Why AI agents need utility functions (00:17:00)

5. Example: AI travel agent and decisions (00:27:00)

6. Linear programming: where to go from here (00:35:00)

7. Closing: Agents and governance (00:36:40)

33 episodes

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