Since the dawn of computing, we have dreamed of (and had night‐ mares about) machines that can think and speak like us. But the computers we’ve interacted with over the past few decades are a far cry from HAL 9000 or Samantha from Her. Nevertheless, machine learning is in the midst of a renaissance that will transform count‐ less industries and provide designers with a wide assortment of new tools for better engaging with and understanding users. These tech‐ nologies will give rise to new design challenges and require new ways of thinking about the design of user interfaces and interac‐ tions.

To take full advantage of these systems’ vast technical capabilities, designers will need to forge even deeper collaborative relationships with programmers. As these complex technologies make their way from research prototypes to user-facing products, programmers will also rely upon designers to discover engaging applications for these systems.

In the text that follows, we will explore some of the technical prop‐ erties and constraints of machine learning systems as well as their implications for user-facing designs. We will look at how designers can develop interaction paradigms and a design vocabulary around these technologies and consider how designers can begin to incor‐ porate the power of machine learning into their work.

Patrick Hebron is a Scientist-in-Residence and Adjunct Graduate Professor at NYU’s Interactive Telecommunications Program. His 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 crea‐ tive reach of its user 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.