Scott Klemmer · 09 November 2006

1. Adaptive Interfaces Scott Klemmer · 09 November 2006

2. Midquarter Evaluation • 8 responses; thanks! • Responders generally enthusiastic about readings and format; one dissenter: “basic literature should not be reviewed” • Three areas for improvement • “not enough time to do all the readings, write the critiques and get enough sleep to go to class and participate” • “Some way to know how we're doing in the class.” (especially with projects) • “I think the student presentations should be more focused on interaction than lecturing” • Overall: Excellent / Very Good / Good / Very Good / Poor / Fair / Very Good / Excellent

3. The Direct Manipulation Ideology • Display as much information as possible • Predictable • Rapid, reversable interactions • User initiates all actions

4. The goal: high information density

5. Command Line: Low density and indirect manipulation

6. guis have improved density and more direct manipulation…

7. …but still have a ways to go

8. Ben Shneiderman on design methods “30 years of planning work in AI is essentially down the tubes because of lack of attention to the user interface. The designers deliver a system and the first thing that the users say is, ‘This is great but what we really want to do is change these parameters.’ The designers say, ‘Well, you know, we didn’t put that in the interface.’ They just haven’t thought adequately about the interface, nor done testing early enough.”

9. The Intelligent Interfaces Ideology • Agents know habits, preferences, interests • Mixed initiative: computer is sometimes proactive • prompt-based telephone interfaces are an example of complete computer initiative

10. Some recent successes • Spam Filtering • Toyota Prius braking system

11. How Spam Filtering Works • Uses a Bayesian network • Begin with a set of ham (good) and spam messages • Look at tokens (email addresses, words) and their relative frequencies in ham and spam • e.g., “mortgage” occurs in 400 of 3,000 spam mails and 5 out of 300 legitimate emails. Its spam probability would be 0.8889([400/3000] divided by [5/300 + 400/3000]).

12. Understanding Intelligent UIs q “Why was this message classified as spam?”

15. Collaborative Filtering • aka recommender systems • Introduced in 1992, roughly simultaneously by… • David Goldberg, Xerox parc (email) • Joe Konstan, Berkeley ->umn (NetNews) • …and explored soon after by many, including • Pattie Maes, mit media lab (music)

16. Traditional DM v. Collaborative Filtering

17. How do they work?An Example Algorithm • Yezdezard Lashkari, Feature Guided Automated Collaborative Filtering, Masters Thesis, MIT Media Laboratory, 1995. • Webhound • Firefly

18. Webhound, Lashkari, 1995

19. Webhound, Lashkari, 1995

20. Webhound, Lashkari, 1995

21. Webhound, Lashkari, 1995

22. Attentional Interfaces • Chris Schmandt (MIT Media Lab) • James Fogarty & Scott Hudson (CMU) • Eric Horvitz (MSR)

23. Everywhere Messaging • C. Schmandt, N. Marmasse, S. Marti, N. Sawhney, S. Wheeler, IBM Systems Journal, 2000 • Several systems • Clues: Finds time-critical emails • Active Messenger: Delivers these to one of many devices • Nomadic Radio: Wearable audio • comMotion: Location-aware

24. Clues

25. Active Messenger

26. Nomadic Radio

27. comMotion

28. Next Time… Capture & Access The Audio Notebook, Lisa Stifelman, Barry Arons, Chris Schmandt Lessons Learned from eClass: Assessing Automated Capture and Access in the Classroom, Jason A. Brotherton and Gregory D. Abowd

29. CS547 Tomorrow David Kirsh, UC San Diego – Cognitive Principles of Design: Effectiveness, Efficiency and Experience