Cyber-Social Learning Systems

89 views

Published on

Kevin Sullivan from the University of Virginia presented: "Cyber-Social Learning Systems: Take-Aways from First Community Computing Consortium Workshop on Cyber-Social Learning Systems" as part of the Cognitive Systems Institute Speaker Series.

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
89
On SlideShare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
4
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Cyber-Social Learning Systems

  1. 1. Cyber-Social Systems Kevin Sullivan University of Virginia February 2, 2017
  2. 2. Overview ● Progress on scientific / engineering principles for cyber-physical systems ○ world of physical systems, computational and connected at all scales ○ radical improvements in system function and performance ● Next frontier in integration of cyber with complex human/social phenomena ● Cyber-social learning systems -- high functioning and continually improving ● Health, healthcare, transportation, education, housing, justice, defense, etc ● Focus on realizing and operating large-scale cyber-social service systems ● With audacious goals: e.g., ○ 10X fewer deaths from avoidable medical errors ○ Most children reading at grade level in the 3rd grade ○ Radical transparency into state and dynamics of cities/communities
  3. 3. Outline ● Distinguish socio-technical, cyber-physical, and cyber-social systems ● Introduce some key elements/assumptions of CSLS perspective ● Survey subset of thoughts on research agenda from CCC WS participants
  4. 4. Socio-Technical Systems ● Systems comprising people, machines, computing, physical elements ● Many service system examples: air-traffic control, healthcare delivery, ed, etc. ● Limited learning at systems scale, often glacial pace of system improvement
  5. 5. Human-in-the-Loop Cyber-Physical Systems ● Central objective is to optimize performance of physical devices/phenomena ● Enabled by advances in sensing and the integration of discrete, logic-based computing with continuous control via hybrid discrete-logic/physics models ● Increasingly use machine learning, and integration of logic and probability ● Often involve humans-in-the-loop to handle control when machine can’t ● Tendency is to progressively reduce human role, to the extent achievable ● Examples: Robotic manufacturing; learning fleets of autonomous vehicles
  6. 6. Example CPS: Mercedes Benz Manufacturing Line
  7. 7. Example of Human-in-the-Loop Learning CPS
  8. 8. Cyber-Physical Systems Learning Loop Tesla Cloud Slower Fleet-Wide Learning Fast Local Control Control Policy and Other Updates Reduced Telemetry Fast Local Control Fast Local Control Fast Local Control ~1B miles of driving data
  9. 9. Tesla - Performance Improvement (lane centering) https://electrek.co/2016/05/24/tesla-autopilot-miles-data/
  10. 10. Tesla -- Performance Improvement (auto-steering) https://electrek.co/2017/01/19/tesla-crash-rate-autopilot-nhtsa/
  11. 11. Tesla Accident, Failure of Human-in-Loop Control
  12. 12. What happened
  13. 13. Trust and Reliance are Human Issues ● Issues of trust in, trustworthiness of, and reliance on AI/autonomy ● Mr. Brown over-trusted the technology relative to its actual capabilities ● Under-trust can be just as harmful; correct calibration of trust is required ● CPS inevitably edge into more complex human/social issues, but CPS foundational theories not designed to deal with such phenomena ● Note: A second, less widely recognized failure mode in this case ○ Car continued autonomous driving after its “shearing” until it hit telephone pole ○ Just evaluating control functions on input sensor data and acting accordingly ○ No awareness whether operating within their envelopes of competence
  14. 14. Cyber-Physical Systems Learning Loop Tesla Cloud Slower Fleet-Wide Learning Fast Local Control Control Policy and Other Updates Reduced Telemetry Fast Local Control Fast Local Control Fast Local Control ~1B miles of driving data
  15. 15. Cyber-Social Learning System for Health (LHS)
  16. 16. Cyber-Social Learning Systems ● Objective is to enable/improve performance of human-intensive systems ○ Public services, healthcare delivery. education, intelligence, defense, food/energy/water, etc ● Enabled by advances in and integration of computational, systems, design and social thinking, policy & law, implementation science, governance, etc. ● Reflective, learning, predictive, adaptive; including norms, policies, goals ● Examples in wild: emerging CSLSs in advertising, social credit, politics
  17. 17. Participant-oriented view (Ben Shneiderman)
  18. 18. Control loop view (influence loop view, and highly simplified)
  19. 19. Rationale for & Scope of CSLS Research Program ● Today, practice outstrips science, engineering, and design knowledge ● Beyond descriptive/predictive theory to prescriptive/interventionist design ● Principles, methods, technologies for CSLS understanding and development ● With impact seen in major improvements in many critical societal systems
  20. 20. Key questions ● What are the critical gaps in knowledge: in theory, systems, applications? ● What kinds of approaches might power & characterize CSLS field? (What unique points of research & technical leverage could enable a transition to a discipline of CSLS design?) ● What methods should be used to evaluate impact of systematic approach to CSLS design against baseline of in-the-wild, ad hoc CSLS emerging today? ● What features in a research roadmap address uncertainties and get to scale?
  21. 21. Overview of ideas submitted ahead of time ● Solicited inputs from all participants ● Enumeration of main topics ● Details of individual contributions ● To help bootstrap discussion, not a final structure
  22. 22. Sampling of research topics of interest ● Motivational structures for technology-mediated social participation in CSLS ● Cyber-social mechanisms for large-scale coordination of complex tasks ● Quality and analysis of data acquired from online social networks/sources ● Humans as sensors and (noisy/biased) information channels/transducers ● Social media exploitation tools as CSLS (distillation, rumor/bias countering, cultural understanding, voice to disenfranchised, cultural understanding) ● Greatly elaborated theories of learning to underpin theory of learning in CSLS ● Principles for integration of human and AI/robotic elements into CSLS
  23. 23. Sampling of interests (continued) ● Organization of computable knowledge at scale for decision support at scale ● Closed loop and person in/on loop system design and evaluation ● Ethical, legal, regulatory aspects of statistical/heuristical deciding machines ● Design for & measurement, control, & assurance of critical system properties ● Designing CSS for ease of observation, experimentation, and adaptation ● Foundations for multi-disciplinary theory- and data-based system modeling ● Dynamics of public opinion as enabler of action based on knowledge
  24. 24. CCC Workshop Process and Beyond ● Three workshops, the last one just completed ● Formulated gaps, approaches, evaluation ideas for cyber, social, systems ● Workshop materials on CCC workshop web site; report forthcoming ● Engaging universities to explore network of campus-wide initiatives in CSLS ● Interested now in engaging with industry and government more broadly ● Possibility to establish an deep and impactful new area of R&D ● Invite interest and engagement from all sectors
  25. 25. Thank you sullivan@virginia.edu Questions?

×