Episode 1. Today, the fundamentalists reveal themselves to the world. Good news! Generative AI is not new, but the scale of it is. In today's discussion, they discuss rigor of systems and the effects of data quality on machine learning.
The AI Fundamentalists - Episode 1
Show notes
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 Andrew Clark, co-founder and CTO of Monitaur.
- Introduction of the podcast topic.
What is the proper rigorous process for using AI in manufacturing? 3:44
- Large language models and AI.
- Rigorous systems for manufacturing and innovation.
Predictive maintenance as an example of manufacturing. 6:28
- Predictive maintenance and predictive maintenance in manufacturing.
- The Apollo program and the Apollo program.
The key things you can see when you’re new to running. 8:31
- The importance of taking a step back.
- Getting past the plateau in software engineering.
What’s the game changer in these generative models? 10:47
- Can Chat-GPT become a lawyer, doctor, or teacher?
- The inflection point with generative models.
How can we put guardrails in place for these systems so they know when to not answer? 13:46
- How to put guardrails in place for these systems.
- The concept of multiple constraints.
Generative AI isn’t new, it’s embedded in our daily lives. 16:20
- Generative AI is not new, but not a new technology.
- Examples of generative AI.
The importance of data in machine learning. 19:01
- The fundamental building blocks of machine learning.
- AI is revolutionary, but it's been around for years.
What can AI learn from systems engineering? 20:59
- Nasa Apollo program, systems engineering.
- Systems engineering fundamentals world, rigor, testing and validating.
- Understanding the why, data and holistic systems management.
- The AI curmudgeons, the AI fundamentalists.
