Host Gregg Garrett speaks with Michael Roberts to discuss how Michael made the AI Agent interviewed in Episode 164, how he tuned it to be a great podcast guest, and where AI might be going in the future. Michael also shares his “Top 3”: Mark Rorvig, a fellow NASA teammate who was with him in his early days of software development and AI exploration; Sydney Lamb, who helped him explore language and the patterns that emerge; and Ken Stanley, who is part of Generative AI’s origin story. And you have to hear what he says about the power of habit.

SHOW HIGHLIGHTS

During this episode:

(0:00) Introduction

(1:38) Meet Michael

(8:33) What is AI?

The “Top Three”:

(14:11) Mark Rorvig: Fellow NASA teammate who was with Mike in his early days of software development and AI exploration

(18:13) Sydney Lamb: Helped him explore language and the patterns that emerge

(23:35) Ken Stanley: Part of Generative AI’s origin story

Other Points of Interest:

(26:55) Creating the “Competing in the Connecting World” RAG model

(37:49) The tuning process

(44:21) Applying AI to long-form documents

(47:33) The future of AI

You Have to Hear This:

(57:39) The power of habit

LINKS AND RESOURCES

Michael Roberts: LinkedIn | Email

Gregg Garrett: LinkedIn | Twitter | About

CGS Advisors: Website | LinkedIn

ABOUT MICHAEL ROBERTS

Michael Roberts is a Senior Digital Transformation leader with significant expertise in artificial intelligence.

He is a seasoned executive with two decades of experience in the field of artificial intelligence and machine learning, with a focus on leading small teams to achieve excellence and building larger organizations that scale with small teams. During his career journey he has had the privilege of working with great teams at organizations like NASA, The Walt Disney Company, and TomTom. 

His career in technology has spanned a broad range of innovative projects, including designing advanced systems for manned spaceflight, building an experimental search engine in the early days of the Internet, exploring the application of genetic algorithms to dynamic neural networks, and designing a patented approach to graph databases. He has spent most of the last decade building data science teams to tackle real-world problems and has diverse experience helping Fortune 500 companies find practical applications for machine learning in business.