How AI Is Built
#036 How AI Can Start Teaching Itself - Synthetic Data Deep Dive
Dec 19, 2024
Adrien Morisot, an ML engineer at Cohere, discusses the transformative use of synthetic data in AI training. He explores the prevalent practice of using synthetic data in large language models, emphasizing model distillation techniques. Morisot shares his early challenges in generative models, breakthroughs driven by customer needs, and the importance of diverse output data. He also highlights the critical role of rigorous validation in preventing feedback loops and the potential for synthetic data to enhance specialized AI applications across various fields.
48:11
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Quick takeaways
- Synthetic data enables faster and cheaper development of specialized AI systems by distilling training data from advanced models like GPT-4O.
- Maintaining high-quality synthetic data requires a combination of human oversight and automated evaluation to ensure accuracy and diversity in training samples.
Deep dives
The Role of Synthetic Data in AI Development
Synthetic data plays a crucial role in the training of large language models (LLMs), allowing for faster and cheaper model development. Large labs leverage more advanced models, like GPT-4O, to generate training data for smaller, task-specific models, a process referred to as distillation. This approach enables the creation of specialized AI systems that do not require extensive datasets, addressing a significant challenge in AI development. The ongoing evolution of synthetic data aims to democratize the training of specialized AI without the burden of collecting vast amounts of real data.
