All ski tracking apps can record information about your day on the slopes, including top speed and distance traveled. More importantly, they offer advanced analysis, such as differentiating between time spent on the lift and actual skiing. However, they rely on GPS data instead of utilising the phone’s internal sensors, which provide more precise data. Could a machine learning model provide more accurate analysis by relying on sensor data?

A Chess-AI is something every programmer should build at least once and Sebastian Lague’s competition was the perfect opportunity for me to do so. Constrained to 1024 tokens I wanted to implement an evaluation function similar to that of Stockfish’s NNUE but heavily simplified. And instead of using a complex feed-forward algorithm to train the neural network I wanted to optimise the parameters using a genetic algorithm that relied on historical Stockfish data. However, I quickly realised why the model Stockfish uses has 50MBs of data and would not fit into my small bot for the competition…