#AI

Transhumanism is the faith that seeks to reset humanity's biological shortcomings through the power of technology. By donning prosthetics and nanomachines, anyone is said to gain the self-perception of a superhero. Abandoning the flesh and kneeling in the temple of silicon and genetic manipulation resembles an adventure that conflates future fantasies with religion. Yet the human-machine hybrid may be a breakthrough or a Pandora's box of the unknown. In the end, the real terror lies not in how technology will change humans, but in how humans will be changed by technology.

Unsupervised learning is an academic sadism that revels in watching data flail about without guidance. Like feudal-era wanderers, data embark on self-imposed trials to form their own tribes. No correct answers or evaluation metrics are provided, leaving only endless parameter tuning and unending debates. The clusters produced sometimes bear meaning, and other times resemble nothing more than lost wanderers in the data wilderness.

Word embedding is a technique that forcefully converts individual words from the sea of strings into coordinates, allowing machine learning models to feign 'understanding' of meaning. By wielding the magic of statistics and the brute force of linear algebra, the resulting vectors carry only a vague promise of 'maybe somewhat similar.' No one bothers with actual semantics, as the model relentlessly learns while paying the daily penalty of computational cost. In the backstage of NLP, it can be seen as an alchemist of language, turning the illusion of words into numbers.

Word2Vec is a model that boasts of lining up words in a vector space with ‘numeric magic’, yet actually draws a crude map based on co-occurrence alone. Researchers peer into this map as if uncovering profound insights, only to use it for the mundane task of labeling similar words. While it professes to understand language, in practice it indulges in search highlighting and recommendation amusements. Celebrated as versatile, it remains powerless against unknown vocabulary gaps.