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 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/

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