With "Agency + Automation: Designing Artificial Intelligence into Interactive Systems", Jeffrey Heer gave a talk about designing interactive systems, and the role of human interaction and machine learning during this process.
Using three case studies - exploratory visualization, data wrangling, and natural language translation - Heer demonstrated that the balanced interplay of strengths and weaknesses is of great importance for a successful adaptive collaboration between humans and computational agents. Ideally, the two agents enrich each other in these processes.
Heer statet that the design of interactive systems benefits of shared representations of tasks augmented with predictive models of human capabilities and actions.
Jeffrey Heer is the Jerre D. Noe Endowed Professor of Computer Science & Engineering at the University of Washington, where he directs the Interactive Data Lab and conducts research on data visualization, human-computer interaction and social computing. The visualization tools developed by Jeff and his collaborators (Vega, D3.js, Protovis, Prefuse) are used by researchers, companies, and thousands of data enthusiasts around the world. Jeff's research papers have received awards at the premier venues in Human-Computer Interaction and Visualization (ACM CHI, ACM UIST, IEEE InfoVis, IEEE VAST, EuroVis). Other honors include MIT Technology Review's TR35 (2009), a Sloan Fellowship (2012), an Allen Distinguished Investigator Award (2014), a Moore Foundation Data-Driven Discovery Investigator Award (2014), and the ACM Grace Murray Hopper Award (2016). Jeff holds B.S., M.S., and Ph.D. degrees in Computer Science from UC Berkeley, whom he then "betrayed" to join the Stanford faculty (2009–2013). He is also a co-founder of Trifacta, a provider of interactive tools for scalable data transformation.