Automatic IQ estimation using stylometry methods.

Stylometry is a study of text linguistic properties that brings together various field of research such as statistics, linguistics, computer science and more. Stylometry methods have been used for historic investigation, as forensic evidence and educational tool. This thesis presents a method to automatically estimate individual’s IQ based on quality of writing and discusses challenges associated with it. The method utilizes various text features and NLP techniques to calculate metrics which are used to estimate individual’s IQ. The results show a high degree of correlation between expected and estimated IQs in cases when IQ is within the average range. Obtaining good estimation for IQs on the high and low ends of the spectrum proves to be more challenging and this work offers several reasons for that. Over the years stylometry benefitted from wide exposure and interest among researches, however it appears that there aren’t many studies that focus on using stylometry methods to estimate individual’s intelligence. Perhaps this work presents the first in-depth attempt to do s

Unexplainability and Incomprehensibility of Artificial Intelligence

Explainability and comprehensibility of AI are important requirements for intelligent systems deployed in real-world domains. Users want and frequently need to understand how decisions impacting them are made. Similarly it is important to understand how an intelligent system functions for safety and security reasons. In this paper, we describe two complementary impossibility results (Unexplainability and Incomprehensibility), essentially showing that advanced AIs would not be able to accurately explain some of their decisions and for the decisions they could explain people would not understand some of those explanations