Description:

Machine learning is in the midst a renaissance. Technical advancements in machine learning are enabling a broad assortment of new possibilities for designers to better engage with and understand users. Yet, these technologies also bring new challenges and require new ways of thinking about the design of user interfaces and interactions. Using intuitive analogies and tangible examples, Patrick Hebron offers an introduction to contemporary machine-learning systems and explores how these emerging capabilities will transform the next generation of computing interfaces, such as search engines, intelligent assistants, connected homes, and open-world video games. This webcast is intended for user interface and user experience designers as well as anyone interested in how recent advancements in machine learning can radically enhance software capabilities through natural language processing, image recognition, content personalization, and behavior prediction.

About Patrick Hebron

Patrick Hebron is a scientist in residence and adjunct graduate professor for NYU's Interactive Telecommunications Program. Patrick's research relates to the development of machine-learning-enhanced digital design tools. He is the creator of Foil, a next-generation design and programming environment that aims to extend the creative reach of its users through the assistive capacities of machine learning. Patrick has also worked as a software developer and design consultant for numerous corporate and cultural institution clients including Google, Oracle, the BMW Guggenheim Lab, and the Edward M. Kennedy Institute.