Everyone has an innate sense of aesthetics and design, a feel for what is pleasing or useful. But many of us lack the time or know-how to fully realize our ideas through design software. Contemporary consumer-level design tools tend to take a “one size fits all” approach, simplifying design processes by forcing users into a limited handful of pre-ordained templates. Professional tools tend toward the “kitchen sink” approach, offering an overwhelming number of highly granular features that come with a steep learning curve and often do not coincide with the user’s own way of thinking.
Patrick Hebron investigates how machine learning and artificial intelligence will bring about a new generation of design tools that learn from and adapt to the user’s way of thinking. This new form of design software will allow both the simplicity of consumer-level tools and the capacity of professional tools by learning from the our behavior to determine the right tools for our work, in addition to helping us organize our thoughts, navigate the numerous interrelated components of a complex project, and quickly visualize the branching possibilities that stem from each design decision we make.
Patrick considers emergent interfaces, design by description, and design through exploration as new paradigms for extending the reach of novice and expert designers alike, exploring how these paradigms can support users in building expertise about design itself rather than the details of a particular interface. Patrick concludes by looking at user interface concepts and interaction models that draw upon recent advances in machine learning to amplify rather than replace human intelligence and creativity.
Patrick Hebron is a scientist in residence and adjunct graduate professor in 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 worked as a software developer and design consultant for numerous corporate and cultural institution clients, including Google, Oracle, Guggenheim/BMW Labs, and the Edward M. Kennedy Institute.
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