Conditional generation enhances synthetic data by allowing data scientists to generate targeted, realistic datasets with specific characteristics, helping improve model training, testing, fairness, risk management and trustworthy AI.
Part 4 in the Demystifying Recent AI Advances Series The previous three posts in this series covered what the models are, how much autonomy to grant them, and how to ground them in your data. This one covers the constraint that quietly governs all three decisions: cost. For the first
Part 3 in the Demystifying Recent AI Advances Series Part 1 of this series covered the models themselves, and Part 2 covered the autonomy and plumbing that turn a model into an agent. This post addresses the question that determines whether any of it produces trustworthy output. Where does the
