Tag: 20

Finally some success after an year of research in trying to teach a computer how to understand a question like a human.

Like I always believed, there is a way to represent each and every natural language sentence using mathematical notations. Its been an year since I have been playing around with various AI algorithms and after countless failed attempts today I made some progress. A sentence can be divided into three parts as explained in Subject-Verb-Object. So a good AI who […]

DIKW Pyramid – Could this be a clue towards building a thought machine? 30 – July

The DIKW Pyramid, also known variously as the “DIKW Hierarchy“, “Wisdom Hierarchy“, the “Knowledge Hierarchy“, the “Information Hierarchy“, and the “Knowledge Pyramid“, refers loosely to a class of models for representing purported structural and/or functional relationships between data, information, knowledge, and wisdom. “Typically information is defined in terms of data, knowledge in terms of information, and wisdom […]

Grus analysis using most used english words – 19th July 2016

Understanding thoughts on the basis of Nouns or Verbs is not taking me anywhere to close building a thought machine. Trying to understand how the most widely used words in English sentence might make a difference I am not sure. This is what I have from Wikipedia: https://en.wikipedia.org/wiki/Most_common_words_in_English $scope.analysiswords = [“the”, “be”, “to”, “of”, “and”, “a”, “in”, “that”, “have”, […]