Stanford Webinar - Large Language Models Get the Hype, but Compound Systems Are the Future of AI

For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai

In recent years AI has taken center stage with the rise of Large Language Models (LLMs) that can be used to perform a wide range of tasks, from question answering to coding. There is now a strong focus on large pretrained foundation models as the core of AI application development. But on their own, these models don’t do much besides taking up significant disk space—it’s only when they’re embedded within larger systems that they start to deliver state-of-the-art results.

In this webinar, Professor Christopher Potts will discuss how AI systems built with multiple interacting components can achieve superior results compared to standalone models. He will also examine how this systems approach impacts AI research, product development, safety, and regulation.

View AI Professional Program: https://online.stanford.edu/programs/...

Chapters:
00:00 - Introduction
00:14 - The Present and Future of Compound Systems
00:38 - Large Language Models and Industry Trends
00:55 - The Impact of GPT-3 on AI
01:07 - Google PaLM and Model Announcements
01:41 - OpenAI's Transition to Systems Thinking
02:01 - Building Effective AI Systems
02:23 - Minimal System for Model Interaction
02:56 - Importance of Prompting and Sampling Methods
03:22 - Various Sampling Techniques
04:04 - Chain-of-Thought Reasoning
04:30 - Majority Completion Strategies
05:00 - Exploring Innovative Sampling Techniques
05:37 - Importance of Systems Thinking
05:56 - Tool Access and System Design
06:40 - Understanding the Evolution of Google Search
06:58 - Scaling Systems for AI
07:53 - Learning from Past Experiences
08:04 - Guardrails and Regulation
09:53 - The Future Impact of AI on Society
10:34 - Insights for Technical and Business Leaders
11:18 - DSPy Learning Resources
12:00 - Final Thoughts on Systems Thinking
12:38 - Conclusion and Q&A Session