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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