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 modeling best practices and rigor.
• What is AI and subsequently LLM regulation going to look like for tech organizations? 5:55
  • Recommendations for reading on the topic.
  • Andrew talks about regulation, monitoring, assurance, and alarm.
• What does it mean to regulate generative AI models? 7:51
  • Concerns with regulating generative AI models.
  • Concerns about the call for regulation from Open AI.
• What is data privacy going to look like in the future? 10:16
  • Regulation of AI models and data privacy.
  • The NIST AI Risk Management Framework.
  • Making sure it's being used as a productivity tool.
  • How it's different from existing processes.
• What’s different about these models vs old models? 15:07
  • Public perception of new machine learning models vs old models.
  • Hallucination in the field.
• Does the use of chatbots change the tendency toward hallucinations? 17:27
  • Bing still suffers from the same problem with their LLMs.
  • Multi-objective modeling and multi-language modeling.
• What does truth-based AI look like? 20:17
  • Public perception vs. modeling best practices
  • Knowledge graphs vs. generative AI: ideal use cases for each
• Algorithms have a really interesting potential application which is a plugin library model. 23:00
  • Algorithms have an interesting potential application.
  • The benefits of a plugin library model.
• What’s the future of large language models? 25:35
  • Practical uses for ML and knowledge base knowledge databases.
  • Predictions on ML and ML-based databases.
  • Finding a way to make LLM useful.
  • Next episodes of the podcast.

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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.

What did you think? Let us know.

Good AI Needs Great Governance

Define, manage, and automate your AI model governance lifecycle from policy to proof.

Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.

Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics:

• LinkedIn - Episode summaries, shares of cited articles, and more.
• YouTube - Was it something that we said? Good. Share your favorite quotes.
• Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.