1. HCI Issues in eXtreme Computing James A. Landay Endeavour-DARPA Meeting, 9/21/99

2. 2 UC Berkeley Endeavour Project HCI in the eXtreme Computing Era Future computing devices won’t have the same UI as current PCs –wide range of devices small or embedded in environment often w/ “alternative” I/O & w/o screens special purpose applications –“information appliances” –lots of devices per user all working in concert How does one design for this environment?

3. 3 UC Berkeley Endeavour Project Design Challenges Design of good appliances will be hard –how do you design cross-appliance “applications”? e.g., calendar app.: one speech based & one GUI based Hard to make different devices work together –multiple devices, UIs & modes, which to “display”? How to build UIs for a physical or virtual space? –take advantage of the resources as the user moves Information overload is a major problem –how to just extract what is relevant?

4. 4 UC Berkeley Endeavour Project Key Technologies Tacit information analysis algorithms Design tools that integrate –“sketching” & other low-fidelity techniques –immediate context & tacit information –interface models

5. 5 UC Berkeley Endeavour Project Our Approach Evaluate rough prototypes in target domains –learning –high-speed decision making Build –novel applications on existing appliances e.g., on the Palm PDA & CrossPad –new information appliances e.g., SpeechCoder (w/ ICSI) Evaluate in realistic settings Iterate –use the resulting experience to build more interesting appliances better design tools & analysis techniques

6. 6 UC Berkeley Endeavour Project Domains of Focus Group-based learning –groups of students teach themselves material –“teachers” give structure, diagnose problems, & respond –shown successful outcomes, but doesn’t scale well –key idea: use ubiquitous sensors & activity data to allow teachers to stay aware of activities as class size scales groups to find expertise among other groups Emergency response decision making –respond to fires, earthquakes, floods, hurricanes,... –quickly allocate resources –situation awareness is paramount –key idea: use activity data to discover & exploit tacit structure user expertise & information quality informal work teams & hierarchies

7. 7 UC Berkeley Endeavour Project Analyze Tacit Activity: Find People & Info The real world –who is talking? who are they looking at? what else is happening? The digital environment –who reads (or writes) what and when? –who communicates with whom and when? with what tools? Goal: Describe an information ecology –people w/ various expertise, backgrounds & roles quickly find human experts (e.g., how to restart pumps…) –documents with content, authority, intended audience… –structures: groups, communities, hierarchies, etc. –visualization that provides awareness without overload –feed this information back to the infrastructure Challenge: recognize/compute from sensor/activity data

8. 8 UC Berkeley Endeavour Project Tacit Information Analysis Methods Social Networks –centrality measures for estimating authority Clustering –discovering tacit groups, and related documents

9. 9 UC Berkeley Endeavour Project Use Context: Improve Interaction Services to discover available devices –there is a wall display -> use it for my wearable Choose interaction modes that don’t interfere

10. 10 UC Berkeley Endeavour Project Use Context: Improve Interaction Services to discover available devices –there is a wall display -> use it for my wearable Choose interaction modes that don’t interfere –context understanding services people are talking -> don’t rely on speech I/O user’s hands using tools -> use speech I/O & visual out –use context as a way to search data collected by ubiquitous archiving services -> UI design tools should understand context & support multimodal I/O

11. 1 UC Berkeley Endeavour Project Multimodal Interaction Benefits –take advantage of more than 1 mode of input/output –computers could be used in more situations & places –UIs easier and useful to more people Building multimodal UIs is hard –often require immature “recognition” technology single mode toolkits recently appeared (“good enough”) –hard to combine recognition technologies few toolkits & no prototyping tools -> experts required –this was the state of GUIs in 1980

12. 12 UC Berkeley Endeavour Project Multimodal Design Tools Should Support Rapid production of “rough cuts” –don’t handle all cases –informal techniques sketching/storyboarding “Wizard of Oz” –iterative design user testing/fast mods Generate initial code –UIs for multiple devices –designer adds detail & improves interaction –programmers add code

13. 13 UC Berkeley Endeavour Project Approach: Sketches & Models Infer models from design “sketches” –model is an abstraction of appliance’s UI design Use models to –semi-automatically generate UIs –dynamically adapt apps UI to changing context Model

14. 14 UC Berkeley Endeavour Project Specifying UI Elements w/ “Sketches”

15. 15 UC Berkeley Endeavour Project Combining the Physical & the Virtual

16. 16 UC Berkeley Endeavour Project Combining the Physical & the Virtual

17. 17 UC Berkeley Endeavour Project Specifying Non-Visual Elements How do designers do this now? –speech scripts or grammars (advanced designers only) flowcharts on the whiteboard “Wizard of Oz” -> fake it! –gestures give an example & then tell programmer what it does We can do the same by demonstration

18. 18 UC Berkeley Endeavour Project Specifying Non-Visual Events (Speech)

19. 19 UC Berkeley Endeavour Project Plan for Success Year 1 –evaluate context-aware prototypes in target domains (op6) –test & refine authority mining algorithms (op5) Year 2 –design & implement multimodal UI design tool (op7) –implement tacit mining algorithms using sensing data for (op5) expert locator & query-free retrieval providing visual awareness of group & task clustering –create new applications using the tools for (op6) learning high-speed decision making Year 3 –evaluate tools & applications –integrate with S/W & H/W design tools

20. HCI Issues in eXtreme Computing James A. Landay Endeavour-DARPA Meeting, 9/21/99

21. 21 UC Berkeley Endeavour Project State of the Art Traditional tools & methodologies (paper, VB, …) –no support for multimodal UIs (especially speech) –do not allow targeting one app to platforms w/ varying I/O capabilities (assume like a PC) Model-based design tools –force designers to think abstractly about design Context-aware widgets –how do devices communicate high-level contexts? XML or UIML –still need to understand what should be expressed

22. 2 UC Berkeley Endeavour Project In-Class Group Learning Participatory learning: Students work in groups of 4-7; communicate via pen or keyboard chat –each group has one main note-taker; others add their own comments or questions to the transcript –students can mark up a group transcript, the lecturer’s notes, or a private window –one student per group works as facilitator or TA, posing questions to the others

23. 23 UC Berkeley Endeavour Project Emergency Decision-Making Tacit activity mining (from ubiquitous sensing) –determines where people are, what they are working on, what they know, etc. –quickly find human experts (e.g., how to restart pumps…) –automatic authority mining (quality of information) –visualization that provides awareness without overload Challenge is to recognize and compute structure –we borrow ideas from social network theory