What AI can do for horse-racing ?
This work addresses the horse-racing betting industry and AI research community, but it is incremental as it builds on existing trends without presenting new results.
The paper explores how machine learning, particularly deep learning, can enhance horse-racing predictions and transform the betting market, with potential broader impacts on understanding animal-human interactions and developing non-anthropocentric AI.
Since the 1980s, machine learning has been widely used for horse-racing predictions, gradually expanding to where algorithms are now playing a huge role in the betting market. Machine learning has changed the horse-racing betting market over the last ten years, but main changes are still to come. The paradigm shift of neural networks (deep learning) may not only improve our ability to simply predict the outcome of a race, but it will also certainly shake our entire way of thinking about horse-racing - and maybe more generally about horses. Since 2012, deep learning provided more and more state-of-the-art results in computer vision and now statistical learning or game theory. We describe how the convergence of the three machine learning fields (computer vision, statistical learning, and game theory) will be game-changers in the next decade in our ability to predict and understand horse-racing. We consider that horse-racing is a real world laboratory where we can work on the animal-human interaction and build a non-anthropocentric Artificial Intelligence. We believe that this will lead us to understand the horses better and the interactions between animals and humans in general.