Linguistics, Psycholinguistics and Semantics

Language, in other words the storehouse of all human Knowledge is represented by words and meanings. Language by itself has an Ontological structure, Epistemological underpinnings and Grammar. Across languages, even though words /usages differ, the concept of meanings remain the same in respective communications. Yet the "Meanings" are understood by human beings based on Contextual, Relative, Tonal and Gestural basis. The dictionary meanings or 'as it is' meanings are taken rarely into consideration, thus human language is ambigious in one sense and flexible in other.

Computers on the other hand are hard-coded to go by the dictionary meanings. Thus teaching (programming) Computers to understand natural language (human language) has been the biggest challange haunting Scientists ever since the idea of Artificial Intelligence (AI) came into existance. In addition this has lead to the obvious question of "What is intelligence" from a Computation perspective. Defining intelligence precisely being impossible, this field of study has taken many shapes such as Computational Linguistics, Natural Language Processing and "
Machine Learning" etc. Artificial Intelligence instead of being used as a blanket term, is now being used increasingly as "Analytics" in many critical applications.

Sanskrit being the oldest is also the most Scientific and Structured language. Sanskrit has many hidden Algorithms built into it as part of its vast scientific treatises, for analysing "Meanings" or "Word sense" from many perspectives since time immemorial. "It is perhaps our job to discover and convert the scientific methods inherent in Sanskrit into usable Computational models and Tools for Natural Language Processing rather than reinventing the wheel" - as some Scientists put it. This blog's purpose is to expose some of the hidden intricate tools and methodolgies used in Sanskrit for centuries to derive precise meanings of human language, to a larger audiance particularly Computational Linguists for futher study, analysis and deployment in Natural Language Processing.

In addition, Sanskrit even though being flexible as a human language, is the least ambigious as the structure of the language is precisely difined from a semantical and syntactical point of view. From a Psycholinguistic perspective this blog could also give us a glimpse of the advanced linguistic capabilities of our forefathers as well their highly disciplined approach towards the structure and usage.

Saturday, January 12, 2013

Scientific method - flaws

Some flaws in the so called "Scientific method" of Research

The Scientific method used in Research today as described by Scientists consists of the following 4 iterations.

1. Question - Framing the Question
2. Hypothesis - A proposal based on reason suggesting a possible correlation between or among a set of phenomena (more than one hypothesis is expected but seldom given)
3. Prediction - The logical consequences of the hypothesis
4. Experiment - Only when one can't design an experiment which can disprove the hypothesis the hypothesis stays and becomes the conclusion (answer) to the question. (this is like proving the opposite!)

The scientific method is iterative or supposed to be iterative. But prior to this, what matters is that in the above 4 items essentially have to deal with the 'What', 'Why' and then later comes 'How' - so we question first "the Premise" - the most important starting point of any Scientific method. Note that every scientific theory starts with a premise. It is seldom asked on what basis the "Premise" is chosen for a particular theory

1. Language - what kind of Scientific language - arithmetic, symbols, algebra, FOPL, calculus or simply Natural Language (susceptible to has ambiguity)
2. Ontology - Type of Classification that is and the starting point - where do you stand - with respect to your question - are you in agreement with Newtonian ontology - which is primarily based on Material world and on Reductionism or Einsteinian - which is causality or Quantum theory which is on Probability
3. Epistemology - Logic of logic - when a hypothesis is made, what are the logical guidelines the hypothesis is adhering to and why such a logic is chosen instead of another
4. Computation - The scale - what is the purpose and the method of computing, also the parameters - this will reveal the core purpose of the hypothesis the corresponding  experiment and their relationship - what is trying to be concluded (least for now)
5. Finally the big question - "Is conclusion possible or necessary?" the popular opinion is that Scientists seek conclusion but that's not true, not all Scientists are rushing to conclude - prevalent practice nowadays is that a view is given - which media takes and interprets as conclusion