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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