Lecture Notes: AI Engineering and Applications

Introduction

• Speaker: Matt Turk from FirstMark and Chip Huan, guest
• Topic: Discussion about Chip Huan's new book "AI Engineering"
• Focus: Building production AI applications using foundation models

Key Points Discussed

AI Engineering vs. Traditional ML

• AI Engineering:
  • Built on long-standing techniques but feels new due to recent advancements.
  • Involves using pre-built large language models.
  • More accessible for organizations without needing to build models from scratch.
• Traditional ML:
  • Requires building models from ground up.
  • Heavily data and computation dependent.

Differences in AI Evaluation

• More intelligent AI systems are harder to evaluate.
• Silent failures and evaluation challenges in sophisticated AI systems.

Prompt Engineering

• Often underrated, considered easy but requires skill to be effective.
• Crucial for guiding AI behavior and outputs.

Importance of RAG (Retrieval-Augmented Generation)

• Essential for models unable to process large data sets.
• Still relevant despite increasing context window sizes in models.

Challenges in AI Planning

• Planning involves predicting outcomes of actions.
• Search problem: finding optimal paths or solutions involves complex reasoning.

Hybrid Systems

• Combination of generative AI and traditional ML models.
• Generative models complement traditional classifiers and detectors.

Evaluation Metrics

• Business-Oriented: Must relate to ROI and direct impacts on business.
• Technical: Entropy and Perplexity (though less used in real-world applications).

Training Language Models

• Pre-training Phase: Focus on language modeling tasks.
• Post-training Phase: Fine-tuning for specific human interactions.
  • Reinforcement learning from human feedback.
  • Direct preference optimization.

Sampling

• Process of selecting outputs from many possible options based on statistical likelihood.
• Influences creativity and coherence in AI outputs.

Development and Infrastructure

• Application Layer: Initial development and prototyping.
• Model Development: Fine-tuning and optimizing models.
• Infrastructure Layer: Deployment and scaling considerations.

AI Agents

• Defined by ability to interact and perceive environments.
• Require planning and tool use for executing tasks.

Book and Additional Resources

• Book Title: "AI Engineering: Building AI Applications with Foundation Models"
• Additional Resources: GitHub repository with references and summaries.

Conclusion

• The discussion emphasizes the evolving field of AI engineering and its applications.
• Recognizes the significance of combining traditional machine learning techniques with new AI models.

Note: The lecture covers many technical aspects and practical insights applicable for both technical and non-technical audiences interested in AI engineering and its impact on business and technology.