KitBit: A New AI Model for Solving Intelligence Tests and Numerical Series

TL;DR

KitBit is a novel AI model that efficiently predicts patterns in numerical sequences, including IQ test problems and large OEIS database series, demonstrating high accuracy and speed.

Contribution

Introduces KitBit, a new computational model using minimal algorithms to solve and predict complex numerical sequences in various contexts.

Findings

Successfully solves IQ test sequences in under a second

Predicts next terms in OEIS database sequences with high accuracy

Outperforms existing models in pattern recognition tasks

Abstract

The resolution of intelligence tests, in particular numerical sequences, has been of great interest in the evaluation of AI systems. We present a new computational model called KitBit that uses a reduced set of algorithms and their combinations to build a predictive model that finds the underlying pattern in numerical sequences, such as those included in IQ tests and others of much greater complexity. We present the fundamentals of the model and its application in different cases. First, the system is tested on a set of number series used in IQ tests collected from various sources. Next, our model is successfully applied on the sequences used to evaluate the models reported in the literature. In both cases, the system is capable of solving these types of problems in less than a second using standard computing power. Finally, KitBit's algorithms have been applied for the first time to the complete set of entire sequences of the well-known OEIS database. We find a pattern in the form of a list of algorithms and predict the following terms in the largest number of series to date. These results demonstrate the potential of KitBit to solve complex problems that could be represented numerically.