Synthetic data is generated from broad categories of user requests, allowing the model to explore specific harmful examples and improve its classifiers.

Synthetic data plays a crucial role in drug discovery, especially in chemistry, where we can use theoretical models to create scalable molecular simulations and enhance the information content of our datasets.

High-quality training data is crucial for the success of Deep Research, as the quality of the input data significantly influences the model's output quality.

To create effective AI models for engineering, we need to generate synthetic training data that is physics-based and informed by supply chain considerations for various physical product domains.

Aligning the data distribution in your application workload with the training data distribution of the model is crucial, but it is challenging due to assumptions made by researchers about user needs.

Not all data is created equal; it has different qualities and applications, which means a strategic approach is necessary to utilize it effectively.

Companies can now train AI systems using their own unique data, such as images of their products, which may not be available online.

Fine-tuning AI models on new types of data is crucial to maintain accuracy, especially as trends and terminology evolve rapidly.

By applying the same model structure to different datasets, you can generate unique and interesting product features.