The Future of AI: Accessibility of Transfer Learning and Fine-tuning

In this episode, I had the pleasure of speaking with Jason Liu, an applied AI consultant and the creator of Instructor – an open-source tool for extracting structured data from LLM outputs. We chat about LLM applications, their challenges, and how to overcome them. We also dive into Instructor, making LLMs interact with existing systems and a bunch of other cool things. Join our Discord community: https://discord.gg/tEYvqxwhah ➡️ Jason Liu on Twitter – https://twitter.com/jxnlco 🤖 Instructor Blog – https://jxnl.github.io/instructor/ 🌐 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

Timestamps: 00:00 Introduction 02:18 Excitement about Machine Learning and AI 03:28 Using LLMs as Backend Developers 04:22 Building Applications with LLMs 07:07 Building Instructor 09:30 Thinking in Logic and Design 10:33 Validating Data and Building Systems with Instructor 11:49 Thoughts About Product and UX in LLMs 17:51 Future of Instructor 20:25 Misconceptions and Unsolved Problems in LLMs 24:57 Improving LLM Applications 26:14 RAG as Recommendation Systems 29:32 Fine-tuning Embedding Models 32:32 Beyond Vector Similarity in RAG 39:32 Predictions for the Next Year in AI and ML 45:26 Measuring Impact on Business Outcomes 47:06 The Continuous Cycle of Machine Learning 48:38 Unlocking Economic Value through Structured Data Extraction 50:52 Questioning the Status Quo and Making an Impact