Machine learning’s success paints a picture of the world that is structurally more like the humanities, in which deductive logic struggles because of the abundance of interrelated, nonquantifiable variables. So much of disciplines such as literature and history is about the multitudinous contingent forces that shape unexpected outcomes, from great novels to savage wars. Still, researchers in physics and other “hard” sciences are also leveraging machine learning to analyze complex phenomena such as the faint, noisy signals of gravitational waves, thus helping to enhance detection and refine models of these ripples in spacetime.
Machine learning is showing us the world as composed of the interrelationships of millions of particulars, each governed by the deductive application of principles, but each interacting in ways too complex to be deduced. Together, they give rise to probabilistic truths that can be more useful and valuable than deductions from eternal laws.