A New Model of Artificial Intelligence

Abstract

In this article, I'll present a new model of artificial intelligence rooted in information theory that makes use of tractable, low-degree polynomial algorithms that nonetheless allow for the analysis of the same types of extremely high-dimensional datasets typically used in machine learning and deep learning techniques. Specifically, I'll show how these algorithms can be used to identify objects in images, predict complex random paths, predict projectile paths in three-dimensions, and classify three-dimensional objects, in each case making use of inferences drawn from millions of underlying data points, all using low-degree polynomial run time algorithms that can be executed quickly on an ordinary consumer device. In short, the purpose of these algorithms is to commoditize the building blocks of artificial intelligence. All of the code necessary to run these algorithms, and generate the training data, is available on my researchgate homepage, under the project heading, Information Theory. Note that I retain all rights, copyright and otherwise, to all of the algorithms, and other information presented in this paper. In particular, the information contained in this paper may not be used for any commercial purpose whatsoever without my prior written consent.