Using AI to play games is often perceived as an early step toward achieving general machine intelligence, as the ability to reason and make decisions based on sensed information is an essential part of general intelligence. Games are good playgrounds for experimenting with intelligent agents as the goals, actions, and rules are often well-defined and abstract. People have been interested in using AI to play games for quite a while. The recent development of deep neural networks allowed visual information in games to be processed effectively and directly used for the decision making of agents, and the area of deep reinforcement learning and meta-learning are also being explored in this aspect.
Shengsheng Huang takes you through her experiences from her attempts in using the AI on Spark for playing games. She provides demos and some details of the experiments and what she learned, for example, whether Spark is a good fit for implementing game-related AI, which parts needs to be improved, and the changes of Spark in the area of AI game playing.
Shengsheng (Shane) Huang is a software architect at Intel and an Apache Spark committer and PMC member, leading the development of large-scale analytical applications and infrastructure on Spark in Intel. Her area of focus is big data and distributed machine learning, especially deep (convolutional) neural networks. Previously at the National University of Singapore (NUS), her research interests are large-scale vision data analysis and statistical machine learning.
Help us make this conference the best it can be for you. Have questions you'd like this speaker to address? Suggestions for issues that deserve extra attention? Feedback that you'd like to share with the speaker and other attendees?
Join the conversation here (requires login)
©2019, O'Reilly Media, Inc. • (800) 889-8969 or (707) 827-7019 • Monday-Friday 7:30am-5pm PT • All trademarks and registered trademarks appearing on oreilly.com are the property of their respective owners. • email@example.com