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Center For telecomm research workshop 2018
A brief overview of the work done across the three years of my Ph.D at King's College
Center For telecomm research workshop 2018
- 1. Quantifying the intangible fromData. Sagar Joglekar, Ph.D. Candidate, Computer Science @SagarJoglekar 1
- 2. What is theintangible? ● Drives human cognitive processes. (aesthetics) ● Subject to a deeper perception (Safety, beauty) ● Grounded in context, culture and community. (Support) 2
- 3. What “Intangible” doyou “Quantify”? ● Online: ○ How do we share and seek help online ? ● Offline ○ Can we quantify how citizens perceive spaces around them? ○ Can we use this knowledge to create better cities ? 3
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- 5. Online life isstream of gushing information 5
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- 9. Data: • Online supportcommunities for Mental distresses and respiratory distress • More than 2 million posts over a period of 5 years • More than 100,000 active users between the two. 9
- 10. Key Question: • Canwe operationalize offline theories of Social support, online? • Can we quantify these processes? 10
- 11. Technical challenges Find established theoriesto draw parallels from Find effective operationalization of theories Drawling Causal links* 11 Technical challenges Find established theories to draw parallels from Find effective operationalization of theories Drawling Causal links*
- 12. User Graphs: • Node=> Users • Edges => Who replies to whom • Edge weights => Topical alignment between user pairs 12
- 13. Topic Modelling • Modelingcorpus of text as a mixture of topics • Each topic is a mixture of words. • Represent text as a vector of topics. • Easier understanding of salient phrases
- 15. Taking offline support theories, online Social Capital*-> Embeddedness of help seekers (Topology of community) Critical consciousness *-> Topical relevance of responses (LDA topical coherence of posts) Community capacity* -> Urgency, Mobilization, Involvement (temporal aspects of activity) *Community organizing and community building for health– Meredith Minkler et.a 1997 15
- 16. How embedded arethe support seekers ? Life sucks, I want to kill myself 16
- 17. How embedded are thesupport seekers ? Support seekers are Very Central 17
- 18. Topical Alignment: Life sucks,I want to kill myself Don’t say that, you can make your life better. Life is all about creating experiences. Where: 18
- 19. How topically aligned arethe conversations? Responses are Topically aligned 19
- 20. Propensity to Answer:Z-score Questions = 1/3 Answers = 2/3 20 Supportive agents develop over time
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- 22. Supportive agents addcohesion to the conversations 22
- 23. Key Takeaways • Onlinesupport communities shows characteristics of offline support communities • Online supportive conversations show characteristics of offline supportive conversations • People evolve and take over the mantle as they stay on 23
- 24. Why is thisimportant ? Finding supportive agents in an online community Learn language traits in online support. Fill gaps in the care provider’s lexicon. 24
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- 26. Spaces we occupy,influence our mental well being. 26 So what if A.I. could help us design them ?
- 27. Why Urban design? WellBeing City Planning Profit!! Beauty, Safety, Security 27
- 28. The challenge A.I. decisionsare often ``Black Box’’ in nature Decisions need to be explained in the language of users (Architects, designers, planners) Framework needs to be quickly adaptable and scalable 28
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- 30. How do wedo it? http://facelift.datadissonance.org/ 30
- 31. Transformation Example: Original TemplateTransform Beautified 31
- 32. Do humans findresults actually beautified? Original - Transformed Mturk comparison At least 3 votes per comparison. Overall agreement 78% 32
- 33. The Explainability Metricsfrom Urban planning: •Walkability •Openness •Complexity •Green Spaces 33
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- 38. Still not perfect Generative modelsare very tricky to train No control over how much to maximize to produce realistic image Now way as of now to incorporate zoning laws 38
- 39. Why is thisrelevant ? 39
- 40. Milestones: • Make humanconditions and human processes part of the learning function • Domain specific transparency in A.I. Systems 40
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- 43. 43 Nishanth Sastry (PrimaryAdvisor) Miram Redi, Wikimedia research Daniele Quercia, Bell Labs UK Luca Aiello, Bell Labs UK Anna Simoni, Blizzard Institute, QMUL Pietro Panzarasa, QMUL
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Editor's Notes
- #4 So through my research, I tried to answer such questions, driven by data and methods from inference science and complex networks. In the next part of the talk, I will give examples from each use case mentioned above Reduce Text here.
- #5 We live in a constant state compulsive access to the flux of information and we are getting addicted to it.
- #6 Our goal is to study how people interact Rise of Innovative creative methods Short videos are the new status update.
- #7 What you see may not what you want to see in your state of mind.
- #8 Reality -> anxiety, Depression But we want to look at the positive processes like support and comfort and draw parallels to real lives.
- #9 And there is a reason for that.
- #10 Compress the text.
- #11 Operationalize
- #18 Add formulas for weighted Centrality Label graphs with symbols from formulae
- #19 Explain formula in slide
- #20 Write a different slide with this Jaccard equation.
- #21 Explain Zscore Explain symbols with some text
- #23 Spell out what we are measuring
- #24 Too much text. Online equal to offline in terms of of conversations and community stricture People mature in their roles
- #27 Urban Mind,
- #28 Creating places that look beautiful or safe, based on people’s perception. Unique combination of using Crowd perception to help design create real world places can be taken as inputs to automated design methods which can not only improve mental health but also would be economically beneficial
- #31 Show slide selectively to unveil each block. Explain each block in relation to what we propose. Explain the formula terms
- #32 Retrieval is done using some deep network We try overlaying the template on the original to give it an additive feel, but did not succeed without corrupting the original
- #34 Not just transform but also help designers improve a place by pointing out changes based on popular urban design metrics (Explainable AI)
- #37 We recruited around 30 experts from the fields of Urban planning, architecture and Urban informatics, to use the app and fulfill certain tasks that required them to get data drivien insights. And then we asked them how useful was such a toll for those tasks.
- #38 Break the slides in to two. Walk through one and then show two side by side
- #41 SMEs are deploying ML for every little task Make human behavior as part of the learning process by either discounting or accounting for it Finally pragmatic transparency, which means explain A.I. decisions in the language of the users
- #42 Every interaction passes through layers of algorithm and a highly curated stream of content comes back. Egotistical inferences from the crowd causes us to consume content that is popular, but not what suits our state of mind. This has given rise to not only phenomena like Filter bubbles and echo chambers, which are huge problems for society in general. So the systems are taking a lot from the users, but not giving any
- #43 Keep human psyche and condition at the center of the learning process. Have a symbiotic relationship.