Baseline modeling is a necessary part of model validation. In our expert opinion, it should be required before model deployment. There are many baseline modeling types and in this episode, we're disc…

In this episode, we explore information theory and the not-so-obvious shortcomings of its popular metrics for model monitoring; and where non-parametric statistical methods can serve as the better op…

In this episode, the hosts focus on the basics of anomaly detection in machine learning and AI systems, including its importance, and how it is implemented. They also touch on the topic of large lang…

We're taking a slight detour from modeling best practices to explore questions about AI and consciousness. 

With special guest Michael Herman, co-founder of Monitaur and TestDriven.io, the team discus…

Data scientists, researchers, engineers, marketers, and risk leaders find themselves at a crossroads to expand their skills or risk obsolescence. The hosts discuss how a growth mindset and "the funda…

Get ready for 2024 and a brand new episode! We discuss non-parametric statistics in data analysis and AI modeling. Learn more about applications in user research methods, as well as the importance of…

It's the end of 2023 and our first season. The hosts reflect on what's happened with the fundamentals of AI regulation, data privacy, and ethics. Spoiler alert: a lot! And we're excited to share our …

Joshua Pyle joins us in a discussion about managing bias in the actuarial sciences. Together with Andrew's and Sid's perspectives from  both the economic and data science fields, they deliver an inte…

Episode 9. Continuing our series run about model validation. In this episode, the hosts focus on aspects of performance, why we need to do statistics correctly, and not use metrics without understand…

Episode 8. This is the first in a series of episodes dedicated to model validation. Today, we focus on model robustness and resilience. From complex financial systems to why your gym might be overcro…

Episode 7.  To use or not to use? That is the question about digital twins that the fundamentalists explore. Many solutions continue to be proposed for making AI systems safer, but can digital twins …

Episode 6. What does systems engineering have to do with AI fundamentals? In this episode, the team discusses what data and computer science as professions can learn from systems engineering, and how…

Episode 5. This episode about synthetic data is very real. The fundamentalists uncover the pros and cons of synthetic data; as well as reliable use cases and the best techniques for safe and effectiv…

Episode 4. The AI Fundamentalists welcome Christoph Molnar to discuss the characteristics of a modeling mindset in a rapidly innovating world. He is the author of multiple data science books includin…

Episode 3.  Get ready because we're bringing stats back! An AI model can only learn from the data it has seen. And business problems can’t be solved without the right data. The Fundamentalists break …

Truth-based AI: Large language models (LLMs) and knowledge graphs - The AI Fundamentalists, Episode 2

Show Notes

• What’s NOT new and what is new in the world of LLMs. 3:10
  •  Getting back to the basics of …

The AI Fundamentalists - Ep1 

Summary

• Welcome to the first episode. 0:03
  • Welcome to the first episode of the AI Fundamentalists podcast.
  • Introducing the hosts.
• Introducing Sid and Andrew. 1:23
  • Introducing …