Recent original work from Google Research introduces breakthrough algorithms designed to significantly enhance user privacy in large-scale data releases for AI and machine learning. Their innovative MaxAdaptiveDegree (MAD) approach optimizes differentially private partition selection by adaptively reallocating 'excess' weight. This method markedly improves the privacy-utility trade-off, allowing for the release of more useful data while upholding robust privacy guarantees. The parallel algorithm demonstrates state-of-the-art results, scales to hundreds of billions of items, and has been open-sourced to foster broader adoption in the field.