  The main topic of this paper is a brief overview of the field of Artificial
Intelligence. The core of this paper is a practical implementation of an
algorithm for object detection and tracking. The ability to detect and track
fast-moving objects is crucial for various applications of Artificial
Intelligence like autonomous driving, ball tracking in sports, robotics or
object counting. As part of this paper the Fully Convolutional Neural Network
"CueNet" was developed. It detects and tracks the cueball on a labyrinth game
robustly and reliably. While CueNet V1 has a single input image, the approach
with CueNet V2 was to take three consecutive 240 x 180-pixel images as an input
and transform them into a probability heatmap for the cueball's location. The
network was tested with a separate video that contained all sorts of
distractions to test its robustness. When confronted with our testing data,
CueNet V1 predicted the correct cueball location in 99.6% of all frames, while
CueNet V2 had 99.8% accuracy.
