• Linguistics,  Philosophy,  Religion

    The Problem of Scriptural Exegesis

    Exegesis, according to Wikipedia, is “a critical explanation or interpretation of a text, particularly a religious text”. In the Vedic tradition, it exists as the commentaries by previous āchāryās who have explained the scriptures in various ways according to time, place, and circumstances. Such commentaries are essential for one key reason—the meanings of the words are continually evolving with time, and if we simply read the original text, we might interpret it according to the present-day meanings of the words, which might not be the meanings as were previously intended. This post discusses the consequences of changing meanings on the understanding of scriptural knowledge.

  • Biology,  Linguistics,  Psychology

    The Phonosemantics Thesis

    In earlier posts—such as here—I described the notion of space in which words are identical to their meanings, and connected it to a tree-like structure of space. In the last post I described how this tree like structure of space appears in all languages in trying to decode their meanings. In this post I will briefly discuss the empirical evidence that supports the notion that meanings are derived from the sounds of phonemes. In contrast to the conventional wisdom in linguistics which claims that the connection between sounds and meanings is arbitrary, this post describes how a closer analysis of linguistic roots suggests otherwise. This topic is broadly called Phonosemantics or “sound symbolism”.

  • Computing,  Linguistics

    The Problem of Meaning in Artificial Intelligence

    Since the 1960s, when computers first appeared,  a machine that can think just like humans was claimed to be just a few years away. This idea has been called Artificial Intelligence (AI) and it reappears every few years in a new form, the latest being the brouhaha around “Machine Learning”, “Deep Learning”, etc. The algorithms and techniques underlying these trends have existed for a few decades, and their limitations are also well-known. However, even with growing computational power we are only able to get closer to the boundaries of what is possible, rather than cross into what is impossible. This post discusses the problems which cannot be solved by AI in…