1. Intelligent User InterfacesPattie MaesFluid Interfaces Group

2. Definition: Intelligent user interfaces are human-machine interfaces that aim to improve the efficiency, effectiveness, and naturalness of human-machine interaction by representing, reasoning, and acting on models of the user, domain, task, discourse, and media.

3. IUI range of approaches: Personalized, proactive interfaces Personalization User modeling/user profiling Adaptive interfaces (machine learning) Recommender systems Software agents � Interfaces to mimic human-to-human interaction Conversational interfaces, natural language techniques in interfaces Multi-modal input and output Personified, anthropomorphic interfaces Interfaces that recognize affect Interfaces with common sense Interface that plan and reason Main conference: annual IUI conference

4. Concept Videos Knowledge Navigator, Apple, 1987 http://www.youtube.com/watch?v=3WdS4TscWH8 Future Shock, Apple, 1988 http://www.youtube.com/watch?v=QcYrPkFe2J0 Starfire Video, Sun, 1994 http://asktog.com/starfire/starfire.mp4 �

5. Contentious Issue #1 Is personification good or bad? Microsoft Bob: http://www.youtube.com/watch?v=ZegWedG-jk4 Microsoft Clippy: http://en.wikipedia.org/wiki/Office_Assistant Microsoft�s Ms Dewey: http://www.youtube.com/watch?v=QPDvfPb2SqE&feature=related Nicole: http://www.youtube.com/watch?v=Sgg-WkfSOxY Multi-touch Barcelona �real human interface�: http://vimeo.com/4697849

6. Contentious Issue #2 Is the problem �AI complete�?

7. Contentious Issue #3 Trust & predictability? awareness of functionality predictability of outcome understanding limitations user should be in control reporting/explanation by the system able to turn off, modify system

8. Contentious Issue #4 Privacy? user-ownership of data/agents user determines what is made available and to whom

9. Contentious Issue #5: Cost/benefit tradeoff High likelyhood of being relevant/useful to the user offered as unobtrusive as possible Minimize disruption, distraction Ignorable (ramping interface) requires minimal user effort to access Challenges in: Natural, �on-the-move� interfaces

10. Techniques Used in Profile Building & Recommendations Explicit user input of preferences, interests Statistical & data-mining approaches: recognizing patterns in data of: one user many users Using background knowledge (common sense knowledge) to infer/learn/generalize

11. Intelligence in commercial applications Recommendation systems are ubiquitous Typo & grammar correction Automated bidding Spam filters More, better speech interfaces �