Computation for the Corridor

1. Computation forthe Corridor Bringing Social Software to Social Spaces Max Van Kleek Research Qualifying Examination January 2005

2. motivation why public spaces and circulation routes?

3. High traffic public spaces harbor thegreatest opportunity for chance encountersthat can lead to: new acquaintances unplanned meetings informal collaborations One of the primary wayspeople build networks of casual acquaintances within their organization

4. knowledge workers need to collaborate today more than ever before Knowledge worker : highly skilled participants of an economy where information and its manipulation are the commodity and the activity. Examples: researchers, engineers, designers, architects product developers, resource planners, legal counselors, financial consultants, teachers, clerks Contrast with: makers of physical goods or services P. Drucker (1959)

5. knowledge workers need to collaborate today more than ever before “Coming Age of Social Transformation” (socioeconomic theory by Peter Drucker, 1959) Knowledge workers equipped with increasingly specialized skillsets, while facing broad challenging problems Collaborations form around exchanging expertise/ sharing of skillsets; lets workers achieve goals more efficiently Collaborations more like “consulting sessions”: Spontaneous, small, loose-knit and short-term; Among “familiar strangers”

6. knowledge workers need to collaborate today more than ever before But how do knowledge workers find collaborators? out of context ! through friends of one’s supervisor casual social acquaintences in line at the cafeteria waiting for the elevator at the water cooler IBM - “method to this madness” knowledge discovery server (2000) automated “knowledge management”

7. Informal meetings occur frequently at work SteelcaseWorkplace Index Survey - (2002)977 full-time employees at various “knowledge-driven” companies • spent 50% of day working away from desks • Remaining time was spent “collaborating, and holding impromptu meetings in secondary spaces, such as hallways, enclaves and water coolers” • 64% preferred standing, reclining or leaning while engaged in impromptu meetings than meeting at their desks • wished employers provided a greater range of seating products and meeting spaces that were conducive to such meetings.

8. Social proximity correlates with physical proximity and likelihood of mutual collaboration • R. Kraut, C. Egido “Patterns for Contact and Communication in Scientific Research Collaboration”, ACM CSCW 1988.

9. Background Other recent trends breakdown in sense of community • Overcrowding • Telecommuting • Disjoint workspaces R. Kraut, C. Egido “Patterns for Contact and Communication in Scientific Research Collaboration”, ACM CSCW 1988.

10. Meeting new people is important “Strength of Weak Ties” (sociological theory by Mark Granovetter, 1973): personal: more opportunities come from one’s “weak ties” than close friends; “personal success” correlated with size of social network organizational: networks of weak ties facilitate knowledge flow across cliques within organizations; promotes cohesiveness and organizational memory

11. yet today, organizations remain poorly socially connected

12. How well do you know your CSAIL colleagues?(In-house validation survey 1 faculty, 1 staff, 8 students, sept 2003) How many people are you acquainted with on this floor? On neighboring floors? On other floors? a) 10-75%: mean 52%; b) < 10%; c) 0-1%, How often do you like to share links (not-directly-work-related items) with lab colleagues. How do you do it? Most: Several times a day. A couple: a few times a week. One: once a month 1: Word of mouth; 2: IM; 3: e-mail. How do you disseminate information to the entire lab? How often? Rarely, email all-ai, “inappropriate”; Paper posters

13. Supplement “accidental” f2f interactions with various software social software to the rescue? Instant messaging Portholes IBM Knowledge Discovery Server Ambient Awareness Shared Media Spaces Montage

14. But… the desktop is the wrong place for social software Desktop : Work Context Competes with productivity for focus of attention Information overload Too many distractions already Ties users to their desks Workers need to take breaks regularly for their own health MIT RSI guidelines: 1-2 mins every 15 minutes 5-10 minutes every two hours Provide a good opportunity for social activity

15. Social software belongs in social spaces.

16. applications

17. k:info: billboard/screen saver

18. (user) I think the User wants I think the User wants Breaking News #32 You are all wrong. She wants to know about free Online sources It’s Monday. Give user a break! cameras, microphones motion sensors other perceptual inputs interaction history display schedule knowledge sources and recommenders k:info, the “smart” billboard

25. serendipity:making k:infomore social

27. skinni:an ‘information kiosk’

33. architecture

34. ontogen: an ontology language for metaglue

36. interfaces

37. distinctive touch:identifying usersby gesture

38. >> min(times) 1.0290 0.4060 1.5410 0.5180 0.3540 1.1390 1.3990 1.0310 2.2330 1.0930 >> mean(times) 1.3309 0.9380 2.4711 0.9367 0.5284 1.4328 1.5674 1.1913 2.4735 1.3355 >> max(times) 2.0140 1.5030 2.9120 1.3070 0.6900 1.8130 1.9180 1.3330 2.8690 1.7490 >> std(times) 1.2539 0.3313 1.1927 4.3841 2.1382 0.3453 2.2351 0.3209 0.7660 1.6609 >> mean(mean(times)) 1.4206 >> vstd(times) 0.6467 < 5 seconds

39. training doodles feature extraction build amodel/trainclassifier trainedclassifier “max!” building a dt classifier

40. feature extraction dean rubine: specifying gestures by example 13 unistroke features: 11 geometric, 2 time

41. training set: hiragana character set train set: 45 classes 10 ex each 1-5 strokes test set: 45 classes 5 ex each

42. strokewise ML- simplest multistroke generative model represent each class as separate sequences of states, each representing a stroke. Each state thus has an associated parametric distribution over the values of that stroke’s feature vector strictly encodes our previous assumption that strokes of the same class always arrive in the same order... otherwise, we’d need an HMM.

43. strokewise ML- 1 gaussian per stroke performance with individual features

44. strokewise ML- 1 gaussian per stroke performance with multiple features

45. fisher linear discriminant - (1-dimensional) OVA (one-versus-all): train C FLD binary classifiers on the fvs evaluate each one on the test point +1 if it gets the label right, 0 otherwise / (C*N)

46. fisher linear discriminant - combined features (warning: figures are a bit misleading; we’ll describe why in the next section)

47. support vector machines - OSU SVM Toolkit for Matlab [ http://www.ece.osu.edu/~maj/osu_svm/ ] training took too long - no l-o-o

48. comparison k-nearest-neighbors - simple, most sensitive to choice of feature extractors sequential ML - simple to estimate, strictly requires stroke order fisher linear discriminant (1d) - performed well support vector machines (lin, quad kernel) - outperformed other methods, took significant training time

49. rejection knn - greedily chooses k nearest neighbors strokewise ML - chooses largest log likelihood • choose thresholds empirically using l-o-o validation (in theory, tricky in practice - soft thresholds difficult to manage) FLD and SVMs - gauge ‘specificity’ of discriminants by measuring performance as follows: +1 iff all C FLDs/SVMs are correct 0 otherwise => “strict criterion”

50. Speech-based interaction