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.

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---Timestamps:00:00 Intro0:41 State of NLP and LLMs1:33 Repeating the past in NLP3:29 Vector databases vs. classical databases8:49 Choosing the right LLM for an application12:13 Advantages and disadvantages of LLMs16:10 Where LLMs are most useful21:13 The dark side of LLMs and can we detect it?25:19 Thoughts on LLM leaderboard metrics31:19 Using LLMs in regulated industries36:40 Creating a moat in the LLM world40:20 Evaluating LLMs44:20 Impact of LLM on non-english languages48:35 Thoughts on MLOps and getting ML into production56:48 The Hardest Unsolved Problem in ML and AI59:09 Predictions for the Future of ML and AI1:03:25 Recommendations and Conclusion

➡️ Han Lee on Twitter – https://twitter.com/HanchungLee➡️ Han Lee on LinkedIn – https://www.linkedin.com/in/hanchunglee/

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