Stefan Ianta has an executive MBA degree from California State University, East Bay and a Project Management Professional certification from PMI. In over 22 years of business experience he lead IT, operations and consulting projects in financial services, retail, and workforce management.
He is interested in the correlations between resource planning processes in businesses and brains and the possibility of modeling such processes with parallel interconnected modules of question answering programs.
Stefan is investigating how massive parallel arrays of cellular automata, similar to cortical columns, could precompute, like Big Data search engines, partial answers to allow quick and meaningful natural language responses, or suggest appropriate actionable decisions for natural language queries or regular business questions.
Project: Question Generation & Question Answering
The goal of the project is to build a program that generates questions from a text.
This could be used, for example, to test student text comprehension by automatically generating questions out of any texts the students have to read.
The proposed way to do this is to first integrate the Stanford parser into the Wolfram Language, then experiment with the parser to find a way to extract questions from phrases and text until an algorithm is found. The Link Grammar can also be researched as a possible extension to this initial goal.
Favorite Outer Totalistic r=1, k=2 2D Cellular Automaton