SmileyNet -- Towards the Prediction of the Lottery by Reading Tea Leaves with AI

TL;DR

This paper presents SmileyNet, a neural network inspired by positive mood effects, capable of predicting coin flips with 72% accuracy from tea leaf images, outperforming standard models and suggesting potential for lottery prediction.

Contribution

Introduces SmileyNet, a novel neural network with a mood-inspired training approach, demonstrating improved prediction of coin flips and potential lottery outcomes from tea leaf readings.

Findings

SmileyNet achieves 72% accuracy in coin flip prediction.

Standard models like Resnet-34 and YOLOv5 achieve 49% and 53%.

Multiple SmileyNets can be combined to predict lottery outcomes.

Abstract

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.