stanford hci group / cs376 research topics in human-computer interaction Intelligent UIs Scott Klemmer 08 November 2005

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

4 Intelligent UIs The goal: high information density

5 Intelligent UIs Command Line: Low density and indirect manipulation

6 Intelligent UIs guis have improved density and more direct manipulation…

7 Intelligent UIs …but still have a ways to go

8 Intelligent UIs 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 Intelligent UIs 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 Intelligent UIs Some recent successes  Spam Filtering  Toyota Prius braking system

11 Intelligent UIs 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]).

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

13 Intelligent UIs

14 Intelligent UIs

15 Intelligent UIs 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)

16 Intelligent UIs Traditional DM v. Collaborative Filtering

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

18 Intelligent UIs Webhound, Lashkari, 1995

19 Intelligent UIs Webhound, Lashkari, 1995

20 Intelligent UIs Webhound, Lashkari, 1995

21 Intelligent UIs Webhound, Lashkari, 1995

22 Intelligent UIs Attentional Interfaces  Chris Schmandt (MIT Media Lab)  James Fogarty & Scott Hudson (CMU)  Eric Horvitz (MSR)

23 Intelligent UIs 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

24 Intelligent UIs Clues

25 Intelligent UIs Active Messenger

26 Intelligent UIs Nomadic Radio

27 Intelligent UIs comMotion