Discussions: Spring 2020

For our first paper in intelligence, we'll be covering how to detect hate speech using machine learning algorithms. This is a super interesting topic, because hate speech is often hard to define and depends a lot on the context surrounding it. This review paper will cover current state-of-the-art approaches for this problem.

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Many organisms can predict future events from the statistics of past experience, but humans also excel at making predictions by pure reasoning-- integrating multiple sources of information, guided by abstract knowledge, to form rational expectations about novel situations, never directly experienced. Here, we show that this reasoning is surprisingly rich, powerful, and coherent even in preverbal infants.

The ability to perform a detailed decision-making approach based on large quantities of parameters and data is at the core of the majority of sciences. Traditionally, all possible scenarios should be considered, and their outcomes assessed via a logical and systematic manner to obtain accurate and applicable methods for knowledge discovery. However, such approach is typically associated with high computational complexity. In this article we discuss some requirements of artificial intuition and present a model of artificial intuition that utilises semantic networks to improve a decision system.

Piggybacking off our ethics discussion in this week's GBM, we'll be focusing on the ethical considerations surrounding use of artificial intelligence in therapeutic applications, specificially with respect to aiding current state of the art approaches to therapy. Much of the paper discusses surveys and statistics of physicians and their attitudes towards technology, and our discussion will extend this to address the ethical backdrop for the topics mentioned.

This week's paper will investigate a new approach for analyzing psychometric qualities from text. Psychometrics, such as emotions or personality traits, are often subconsciously conveyed through the manner in which people communicate. The authors of the paper propose a new method for analyzing psychometric qualities of authors using a modification of other previously developed architectures for natural language processing and analysis.