In a recent hearing on the state of artificial intelligence (AI) of the Senate Commerce Committee, the Dean of the School of Computer Science at Carnegie Mellon University, Andrew Moore, made the case for the important and often overlooked role of statistics.
In his testimony to the Senate, Moore emphasized the fact that AI involves transforming massive amounts of raw data into usable, actionable information. As a consequence, the future of AI depends on increased public support for the efforts of universities and research institutions to build skills to work with advanced machines and computational processes, which requires both technical and analytical skills.
According to Moore, “The really good ideas of the moment in machine learning and deep learning came out of mathematics and statistics. Without the fundamental work going on by the mathematicians and statisticians around the world, we wouldn’t be where we are. Statisticians, who are often the heroes in AI, need help to progress their field forward as well.”
In assessing some of the critical gaps impeding AI development, Moore noted the need for increased application of AI in social contexts, greater accessibility of data compiled by global search engines for small entrepreneurs, and the continued development of statistical methods and tools to create models for the complex behavior of world systems.