Truth-based AI - LLMs and knowledge graphs

Episode 2. Back to the basics of LLMs and knowledge graphs. Also in this episode, timely discussions about truth-based AI, regulations for AI, and data privacy effects on generative AI

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
Deploying fine-tuned LLM projects is slated to only get easier going forward (Google Cloud)
Instant Plugins for ChatGPT: Introducing the Wolfram ChatGPT Plugin Kit

‍What is AI and subsequently LLM regulation going to look like for tech organizations? 5:55

Building LLM applications for production (Chip Huyen) - Recommended reading
Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond (Yang et al) - Great starting point if you are ready to get technical on the different paradigms used by LLMs and want to see some use cases
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 graph vs. generative AI: ideal use cases for each
Future state of tool-based LLMs that use knowledge graphs to retrieve factual information - ex. with the GPT Plugin libraries on tools like Wolfram Alpha

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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