1. Scott Klemmer 02 November 2004
Intelligent UIs
Scott Klemmer
02 November 2004

2. The Direct Manipulation Ideology
Display as much information as possible
Predictable
Rapid, reversable interactions
User initiates all actions
02 November 2004
Intelligent UIs

3. The goal: high information density
02 November 2004
Intelligent UIs

4. Command Line: Low density and indirect manipulation
02 November 2004
Intelligent UIs

5. guis have improved density and more direct manipulation…
02 November 2004
Intelligent UIs

6. …but still have a ways to go
02 November 2004
Intelligent UIs

7. 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.”
02 November 2004
Intelligent UIs

8. 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
02 November 2004
Intelligent UIs

9. Some recent successes Spam Filtering Toyota Prius braking system
02 November 2004
Intelligent UIs

10. How Spam Filtering Works
Uses a Bayesian network
Begin with a set of ham (good) and spam messages
Look at tokens ( 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 s. Its spam probability would be ([400/3000] divided by [5/ /3000]).
02 November 2004
Intelligent UIs

11. Understanding Intelligent UIs
q “Why was this message classified as spam?”
02 November 2004
Intelligent UIs

12. 02 November 2004
Intelligent UIs

13. 02 November 2004
Intelligent UIs

14. Collaborative Filtering
aka recommender systems
Introduced in 1992, roughly simultaneously by…
David Goldberg, Xerox parc ( )
Joe Konstan, Berkeley ->umn (NetNews)
…and explored soon after by many, including
Pattie Maes, mit media lab (music)
02 November 2004
Intelligent UIs

15. Traditional DM v. Collaborative Filtering
02 November 2004
Intelligent UIs

16. How do they work? An Example Algorithm
Yezdezard Lashkari, Feature Guided Automated Collaborative Filtering, Masters Thesis, MIT Media Laboratory, 1995.
Webhound
Firefly
02 November 2004
Intelligent UIs

17. All automated collaborative filtering algorithms use the following steps to make a recommendation to a user:
02 November 2004
Intelligent UIs

18. Webhound, Lashkari, 1995
02 November 2004
Intelligent UIs

19. Webhound, Lashkari, 1995
02 November 2004
Intelligent UIs

20. Webhound, Lashkari, 1995
02 November 2004
Intelligent UIs

21. Webhound, Lashkari, 1995
02 November 2004
Intelligent UIs

22. Webhound, Lashkari, 1995
02 November 2004
Intelligent UIs

23. Webhound, Lashkari, 1995
02 November 2004
Intelligent UIs

24. Attentional Interfaces
Everywhere Messaging (MIT Media Lab)
Scott Hudson (CMU)
Eric Horvitz (MSR)
02 November 2004
Intelligent UIs

25. Everywhere Messaging
C. Schmandt, N. Marmasse, S. Marti, N. Sawhney, S. Wheeler, IBM Systems Journal, 2000
Several systems
Clues: Finds time-critical s
Active Messenger: Delivers these to one of many devices
Nomadic Radio: Wearable audio
comMotion: Location-aware
02 November 2004
Intelligent UIs

26. Clues
02 November 2004
Intelligent UIs

27. Active Messenger
02 November 2004
Intelligent UIs

28. Nomadic Radio
02 November 2004
Intelligent UIs

29. comMotion
02 November 2004
Intelligent UIs