Are businesses ready for large language models as a path to AI? In this episode, the hosts reflect on the past year of what has changed and what hasn’t changed in the world of LLMs. Join us as we debunk the latest myths and emphasize the import...

Our special guest, astrophysicist Rachel Losacco, explains the intricacies of galaxies, modeling, and the computational methods that unveil their mysteries. She shares stories about how advanced computational resources enable scientists to deco...

Can your AI models survive a big disaster? While a recent major IT incident with CrowdStrike wasn't AI related, the magnitude and reaction reminded us that no system no matter how proven is immune to failure. AI modeling systems are no differen...

Join us as we chat with Patrick Hall, Principal Scientist at Hallresearch.ai and Assistant Professor at George Washington University. He shares his insights on the current state of AI, its limitations, and...

Ready to uncover the secrets of modern systems engineering and the future of AI? Join us for an enlightening conversation with Matt Barlin, the Chief Science Officer of Valence. Matt's extensive background in systems engineering and data lineag...

Explore the basics of differential privacy and its critical role in protecting individual anonymity. The hosts explain the latest guidelines and best practices in applying differential privacy to data for models such as AI. Learn how this metho...

Artificial Intelligence (AI) stands at a unique intersection of technology, ethics, and regulation. The complexities of responsible AI are brought into sharp focus in this episode featuring Anthony Habayeb, CEO and co-founder of Monitaur, ...

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 discussing their use cases, strengths, and weakne...

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 option. Introduction and latest new...

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 language models, the (in)accuracy of data scrapin...

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

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 fundamentals" of AI can help.Our episode s...

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 key assumptions in statistics and data model...

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 outlook for AI in 2024.AI regu...

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 interdisciplinary conversation about bias th...

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 understanding how they work, to ensure that models are ...

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 overcrowded at New Year's, you've been directly affe...

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 really deliver for AI what we know they ...

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 the methods and mindset of the latter can bo...

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 effective use in AI. When even SAG-AFTRA and OpenAI m...

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 including ...

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 down the basics of data from collection ...

Truth-based AI: Large language models (LLMs) and knowledge graphs - The AI Fundamentalists, Episode 2Show NotesWhat’s NOT new and what is new in the world of LLMs. 3:10 Getting back to the basics of ...

The AI Fundamentalists - Ep1 SummaryWelcome to the first episode. 0:03Welcome to the first episode of the AI Fundamentalists podcast.Introducing the hosts.Introducing Sid a...