论文标题
部分可观测时空混沌系统的无模型预测
What AI can do for horse-racing ?
论文作者
论文摘要
自1980年代以来，机器学习已被广泛用于赛马的预测，逐渐扩展到算法现在在投注市场中发挥着重要作用的地方。在过去的十年中，机器学习改变了赛马的投注市场，但主要变化仍在进行。神经网络的范式转变（深度学习）不仅可以提高我们简单地预测种族结果的能力，而且肯定会摇摇我们的整个思考赛马的思考方式，甚至更广泛地谈论马匹。自2012年以来，深度学习提供了越来越多的最先进的结果，从而导致计算机视觉和统计学习或游戏理论。我们描述了三个机器学习领域的融合（计算机视觉，统计学习和游戏理论）如何在未来十年的预测和理解赛马的能力中改变游戏规则。我们认为，赛马是一个现实世界实验室，我们可以在其中进行动物人类的互动并建立非人性化的人工智能。我们认为，这将使我们能够更好地了解马匹和人类之间的相互作用。
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.
