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Game Development requires a vast array of tools, techniques, and expertise, ranging from game design, artistic content creation, to data management and low level engine programming. Yet all of these domains have one kind of task in common - the transformation of one kind of data into another. Meanwhile, advances in Machine Learning have resulted in a fundamental change in how we think about these kinds of data transformations - allowing for accurate and scalable function approximation, and the ability to train such approximations on virtually unlimited amounts of data. In this talk I will present how these two fundamental changes in Computer Science affect game development - how they can be used to improve game technology as well as the way games are built - and the exciting new possibilities and challenges they bring along the way.
Daniel Holden (Ubisoft Montreal)
Daniel Holden is a Machine Learning researcher working at Ubisoft Montréal's Research and Development Lab "Ubisoft La Forge". He completed his PhD in 2017 at The University of Edinburgh with research focusing on how Neural Networks and Machine Learning techniques can be used to produce state-of-the-art character animation systems. At "Ubisoft La Forge" he has helped develop and guide several important Machine Learning research projects, and has previously presented his research at GDC, SIGGRAPH, and SIGGRAPH Asia.