Part 10/13:
- Technical misunderstandings: Overcomplication may arise when attempting to fine-tune large models without understanding the underlying mechanisms—such as low-rank approximations and matrix modifications, which are technical but essential concepts.
He shares insights into model optimization techniques, including the use of low-rank approximations to simplify complex neural networks, making training and deployment more manageable.
Responsible AI, Security, and Privacy
A recurring theme is safeguarding sensitive data and ensuring responsible AI usage:
Content safety: Fine-tuned models must be tested against safety and bias considerations.
Continuous evaluation: Regular testing with responsibility-related tests ensures models refuse unsafe queries.