Balancing Algorithms and Data
The Trade-off Between Advanced AI/ML Models and Simple Linear/Logistic Regressions for Accuracy Understanding the Algorithms Older complex machine learning uses tons of data to train AI. For example, a popular computer-vision data set known as MNIST, designed to train models to read human handwriting, contains 60,000 handwritten images from 0 to 9. Models like ChatGPT require billions of words to learn to produce human-like texts. These methods are expensive and require immense computational power, not to mention data preprocessing. For example, an image dataset, ImageNet, used to train AI tools in visual recognition, contains thousands of manually sorted categories. Advancements in deep learning promise to build smarter models. For example, an algorithm known as “transfer learning” allows to train an AI model to find kidneys
