Join MFF CUNI alumnus and DeepStack co-creator Martin Schmid (EquiLibre Technologies) for a captivating talk exploring the journey of AI from conquering complex games like poker to its cutting-edge application in algorithmic trading. Discover behind-the-scenes stories from the world of poker AI, learn about the evolution of games research at DeepMind, and get an exclusive look at how EquiLibre in Prague is leveraging self-learning AI techniques to revolutionize stock trading.
From Poker AI to Algorithmic Trading: Game Theory and Reinforcement Learning in Action
The presentation will start by delving into the fascinating history of AI in games, with a particular focus on poker AI. As this history is explored, intriguing behind-the-scenes stories will also be shared. Moving forward, the discussion will then explore games research at DeepMind, discussing how the field has evolved and its recent real-world impact. Finally, the current work being done at EquiLibre will be introduced—the application of self-learning AI techniques from games to algorithmic trading.
Bio
Faculty of Mathematics and Physics of Charles University (MFF CUNI) alumnus Martin Schmid co-created DeepStack, the first computer to defeat professional poker players. After working at IBM during his studies, interning at the University of Alberta, and earning a PhD from MFF CUNI, he spent five years at Google DeepMind. In 2022, he co-founded EquiLibre in Prague, where he and other former DeepMind researchers are applying their reinforcement learning algorithms (initially for games like poker) to algorithmic stock trading.