SmileyNet -- Towards the Prediction of the Lottery by Reading Tea Leaves with AI
This is an incremental approach for predicting random events like coin flips and lotteries, but it addresses a problem with limited practical impact due to the inherent randomness and lack of real-world validation.
The paper tackles the problem of predicting coin flips using a novel neural network called SmileyNet, which incorporates a mood-based training phase with smileys and an encouraging loss function, achieving an accuracy of 72% compared to 49% for ResNet-34 and 53% for YOLOv5.
We introduce SmileyNet, a novel neural network with psychic abilities. It is inspired by the fact that a positive mood can lead to improved cognitive capabilities including classification tasks. The network is hence presented in a first phase with smileys and an encouraging loss function is defined to bias it into a good mood. SmileyNet is then used to forecast the flipping of a coin based on an established method of Tasseology, namely by reading tea leaves. Training and testing in this second phase are done with a high-fidelity simulation based on real-world pixels sampled from a professional tea-reading cup. SmileyNet has an amazing accuracy of 72% to correctly predict the flip of a coin. Resnet-34, respectively YOLOv5 achieve only 49%, respectively 53%. It is then shown how multiple SmileyNets can be combined to win the lottery.