According to DeepLearning.AI, Andrew Ng emphasizes the importance of choosing a reasonable algorithm, conducting quick sanity checks, and prioritizing data quality over the latest models when starting an ML project. These strategies are crucial for saving time, minimizing errors, and ensuring a solid foundation for machine learning endeavors.

According to @timnitGebru, the launch of the Data Labelers Association aims to standardize and improve the quality of data labeling, which is critical for training AI models used in the cryptocurrency trading sector. This initiative can enhance the accuracy of AI-driven trading algorithms by ensuring better data quality, potentially impacting trading strategies and market predictions.