Thanks to everyone who responded to my appeal to canvas industry opinion on artificial intelligence (AI) in high performance sport. I received an array of questions and comments, from both practitioners and researchers all over the globe. This post shares the key themes that emanated from the discussion. Going forward, I am delighted to be engaging with Zone7 - a leader in AI application for injury risk forecasting and performance management - to discuss each of these areas further. I will be collaborating with co-founders Tal Brown and Eyal Eliakim, as well as Performance Director Rich Buchanan to dive further into each theme.

Machine Learning is the learning in which a machine can learn on its own without being explicitly programmed. It is an application of Al that provide the system with the ability to automatically learn and improve from experience. Here we can generate a program by integrating the input and output of that program. One of the simple definitions of Machine Learning is "Machine Learning is said to learn from experience w.r.t some class of task T and a performance measure P if learners performance at the task in the class as measured by P improves with experiences." Sometimes Artificial Intelligence and Machine Learning are considered as same but they are somehow different from each other.

Today we'll learn about Q-Learning. Q-Learning is a value-based Reinforcement Learning algorithm. This article is the second part of a free series of blog post about Deep Reinforcement Learning. See the first article here. In this article you'll learn: Let's say you're a knight and you need to save the princess trapped in the castle shown on the map above. You can move one tile at a time.

Deep learning is a crucial step toward achieving true AI--but the human brain still reigns supreme. This may be the first time in AI's history when a majority of experts agree the technology has practical value. From its conceptual beginnings in the 1950s led by legendary computer scientists such as Marvin Minsky and John McCarthy, AI's future viability has been the subject of fierce debate. As recently as 2000, the most proficient AI system was roughly comparable, in complexity, to the brain of a worm. Then, as high-bandwidth networking, cloud computing, and high-powered graphics-enabled microprocessors emerged, researchers began building multilayered neural networks--still extremely slow and limited compared to the human brain, but useful in practical ways.