Machine Learning Algorithm Sidesteps the Scientific Method - The New Stack

It's a technique used by many scientific fields, as it provides a structured guideline to answering a question logically and rationally, using empirical evidence -- an approach that ushered humanity out of the dark ages and into today's era where breakthrough discoveries in physics, astronomy and modern medicine are possible. But are there situations in scientific investigation where the scientific method is not needed? A team of researchers at Princeton University's Plasma Physics Laboratory (PPPL) are now proposing that this is indeed possible -- by using a machine learning algorithm that can predict the physical orbits of planets, without the need for it to be based on the laws of physics. The paper on the work, which was recently published in Scientific Reports, outlines how the team trained a machine-learning algorithm on data about the known orbits of Mercury, Venus, Earth, Mars, Jupiter, and the dwarf planet Ceres. This machine-learning algorithm, paired along with what the team calls a "serving algorithm", was then used to predict the orbits of other planets -- including the parabolic and hyperbolic escaping orbits, of the solar system -- without needing to input Newtonian laws of motion and gravitation. Instead, the approach forms what the team calls a discrete field theory, which models the universe as a kind of "black box."