The Conference for Machine Learning Innovation

An Interdisciplinary Approach to Artificial Intelligence Testing: Development of an Artificial Intelligence Quotient (A-IQ)

This talk originates from the archive. To the CURRENT program
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Tuesday, June 19 2018
11:30 - 12:30

Humanity is more than ever confronted with artificial intelligence (AI), yet it is still challenging to find a common ground. By adopting the term intelligence, it has inherited a myriad of issues from the history of psychological intelligence research. AI research inevitably requires an interdisciplinary approach. Our research project aims to understand, measure, compare and track changes of AI capabilities. Therefore, an Artificial Intelligence Model is derived, understood as a system of capabilities to solve problems. It integrates seven categories: explicit knowledge, language aptitude, numerical and verbal reasoning, working memory, divergent and critical thinking. Forms of thinking are classified following Bloom’s Taxonomy. The Artificial Intelligence Scale is developed, reflecting academic IQ-testing procedures. The evaluation through multiple question and answer categories and individual weighting of both is unique, resulting in the A-IQ score. 

Tests are executed with digital assistants, independent of their ecosystem: Google Now, Siri (Apple), Cortana (Microsoft) and Alexa (Amazon). Results indicate best overall performance by Siri due to strong working memory skills. Among other results on category level, Cortana leads at explicit knowledge. None exhibit critical or creative thinking skills. A solution to automate A-IQ testing is proposed. Weaknesses and implications for future research and practice are discussed.

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