The AI Use Cases Podcast
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AI Customization Paradigm Shift: Fine-Tuning, Long Context Models, and Future Trends
This episode starts with a welcome and an introduction to Chris Chang, focusing on AI customization challenges. The discussion compares fine-tuning and in-context learning and explores the emergence of long context models. The conversation then shifts to the AI customization paradigm shift, emphasizing an ecosystem approach and the democratization of AI. Strategies for evaluating AI, future trends, and closing remarks are also covered.
Key Points
- Customizing AI models for specific enterprise applications remains a significant challenge despite rapid advancements.
- Long context models can process up to 1 million tokens at once, enabling comprehensive few-shot learning examples and reducing reliance on complex systems.
- The shift towards long context models and in-context learning democratizes AI customization, making it more accessible through effective prompt engineering and domain knowledge.
Chapters
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Transcript
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