Why AI Fundamentals? | AI rigor in engineering | Generative AI isn't new | Data quality matters in machine learning

The AI Fundamentalists

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 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.

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