Introduction

In this episode, I speak with Han-Chung Lee, a machine learning engineer with a lot of interesting takes on ML and AI. We dive into the buzz around natural language processing and the big waves in generative AI. They chat about how newcomers are racing through NLP’s history, mixing old school and new tech, and the shift towards smarter databases. Han-Chung breaks it down with his straightforward takes, making complex AI trends feel like coffee chat topics. It’s a perfect listen for anyone keen on where AI’s headed, minus the jargon. Join our Discord community: https://discord.gg/tEYvqxwhah --- Timestamps: 00:00 Intro 0:41 State of NLP and LLMs 1:33 Repeating the past in NLP 3:29 Vector databases vs. classical databases 8:49 Choosing the right LLM for an application 12:13 Advantages and disadvantages of LLMs 16:10 Where LLMs are most useful 21:13 The dark side of LLMs and can we detect it? 25:19 Thoughts on LLM leaderboard metrics 31:19 Using LLMs in regulated industries 36:40 Creating a moat in the LLM world 40:20 Evaluating LLMs 44:20 Impact of LLM on non-english languages 48:35 Thoughts on MLOps and getting ML into production 56:48 The Hardest Unsolved Problem in ML and AI 59:09 Predictions for the Future of ML and AI 1:03:25 Recommendations and Conclusion ➡️ Han Lee on Twitter – https://twitter.com/HanchungLee ➡️ Han Lee on LinkedIn – https://www.linkedin.com/in/hanchunglee/ 🌐 Check Out Our Website! https://dagshub.com Social Links: ➡️ LinkedIn: https://www.linkedin.com/company/dagshub ➡️ Twitter: https://twitter.com/TheRealDAGsHub ➡️ Dean Pleban: https://twitter.com/DeanPlbn