Show HN: I replaced Markov Chains with Biomechanics to predict word transitions (github.com)

In a groundbreaking proof of concept, researcher @Professor-Sam-Sepi0l has replaced traditional Markov chains with a novel approach based on biomechanics to predict word transitions in natural language processing (NLP). This new Biomechanical Determinism hypothesis suggests that language evolution is driven by minimizing caloric and biomechanical effort. By mapping phonemes to physical coordinates such as Place, Aperture, and Voicing, the research calculates the "Energy Cost" of word transitions, proposing that each transition follows a deterministic path shaped by our physiological constraints. Key technical insights reveal that sequences requiring lower biomechanical energy are more likely to conform to standard grammar. Using a 3D Vector Space modeled on the International Phonetic Alphabet, the study demonstrates quantifiable differences in energy costs between natural sentence structures and awkward phrases, with the former requiring less energy. Early results indicate that by incorporating a "Physics of Language" component, NLP models can enhance their next-token prediction capabilities by not only weighing tokens based on semantic probability but also considering their biomechanical feasibility, potentially transforming how AI interprets and generates language.