What makes a great team? How to predict a team’s success? How to discover the next leader? How to design a workspace that boosts creativity? Does playful bright workspace make teams more productive? What makes a happy and stress free workspace? Answers to these questions can dramatically change the way modern organizations operate, manage their employees and measure success. In this talk, I will share how Bell Labs' research is helping organizations to find the answers of the above questions by taking an opportunistic network sensing approach that transforms quantified noise into social signals and uncovers hidden behavioural and communication patterns that exist within and across organisations. I will cover the behavioural inference engine of this system, and share some of the fascinating results based on year long deployments in multiple organisations around the world.
Cybernetics, human-in-the-loop and probabilistic modelling for recommender sy...Eliezer Silva
Talk presented at BRAIN NTNU event
https://brainntnu.no/portfolio/brain-talks-big-data2-2/
Blog post about the talk https://brainntnu.no/2019/02/05/society-of-minds/
Reflections about cybernetics, bias in recommender systems and future of AI
Dr. Prasanna Karhade is currently an Assistant Professor in the Department on Information Systems, Business Statistics and Operations Management at The Hong Kong University of Science and Technology.
What makes a great team? How to predict a team’s success? How to discover the next leader? How to design a workspace that boosts creativity? Does playful bright workspace make teams more productive? What makes a happy and stress free workspace? Answers to these questions can dramatically change the way modern organizations operate, manage their employees and measure success. In this talk, I will share how Bell Labs' research is helping organizations to find the answers of the above questions by taking an opportunistic network sensing approach that transforms quantified noise into social signals and uncovers hidden behavioural and communication patterns that exist within and across organisations. I will cover the behavioural inference engine of this system, and share some of the fascinating results based on year long deployments in multiple organisations around the world.
Cybernetics, human-in-the-loop and probabilistic modelling for recommender sy...Eliezer Silva
Talk presented at BRAIN NTNU event
https://brainntnu.no/portfolio/brain-talks-big-data2-2/
Blog post about the talk https://brainntnu.no/2019/02/05/society-of-minds/
Reflections about cybernetics, bias in recommender systems and future of AI
Dr. Prasanna Karhade is currently an Assistant Professor in the Department on Information Systems, Business Statistics and Operations Management at The Hong Kong University of Science and Technology.
The role of expectations in human computer interactions by emanuel baisireEmanuel Baisire
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Communicating an organisation's requirements in a semantically consistent and understandable manner and then reflecting the potential impact of those requirements on the IT infrastructure presents a major challenge among stakeholders. Initial research findings indicate a desire among business executives for a tool that allows them to communicate organisational changes using natural language and a simulation of the IT infrastructure that supports those changes. Building on a detailed analysis and evaluation of these findings, the innovative CRESUS tool was designed and implemented. The purpose of this research was to investigate to what extent CRESUS both aids communication in the development of a shared understanding and supports collaborative requirements elicitation to bring about organisational, and associated IT infrastructural, change. This paper presents promising results that show how such a tool can facilitate collaborative requirements elicitation through increased communication around organisational change and the IT infrastructure.
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3. Computational Rationality as a Theory of Interaction
Interaction
emerges
Actions
Thoughts
Beliefs
Goals
Capabilities
Design
4. Computational Rationality as a Theory of Interaction
Credit: Alex Jung / Aalto University
RL agents adapt via interaction
But AI research
has no interest
in explaining
human
adaptation
Agents try to
learn sequential
action policies
that maximize
rewards
5. Computational Rationality as a Theory of Interaction
Example: Adaptation in typing
Jokinen et al. CHI’21
Write fast w/out error
Adapts when error
occurs Turn on autocorrection
No human data
needed for
training
6. Computational Rationality as a Theory of Interaction
Increasing interest in HCI
Text entry Multitasking Decision-making
Theoretical
commitments?
Core modeling
ideas?
7. Computational Rationality as a Theory of Interaction
Combines RL with cognitive
architectures
Lewis et a al. 2014; Gershman et al. 2015;
Lieder & Griffiths 2019
Computational
Rationality
Cognitive
architectures
Agents act via
their bounded
cognition
Reinforcement
learning
Bounded
optimality
Interaction
emerges as
adaptation within
internal and
external bounds
8. Computational Rationality as a Theory of Interaction
Cognitive Architectures Computational Rationality
An information processor An agent
Manually specified rule set Policy obtained via optimization
Bounded by cognition Bounded by cognition
Program POMDP
Comparison with cognitive
architecture models
9. Computational Rationality as a Theory of Interaction
Theoretical commitments in HCI
1. Agent faces a stochastic
sequential decision problem
2. Agent acts in the world via
its internal environment
3. Bounds exist on information
processing in the internal
environment
4. Agent is boundedly optimal
10. Computational Rationality as a Theory of Interaction
Agent structure point-of-view
Standard RL
Computational rationality
11. Computational Rationality as a Theory of Interaction
Policy estimation view
Lewis & Howes 2019
Subjective utility payoff
surface
Boundedly optimal
policy
Standard RL Computational rationality
Behaviors admitted by
the internal
environment
Behaviors admitted by
the external
environment
Behaviors admitted by
the external
environment
12. Computational Rationality as a Theory of Interaction
Modeling in practice
External environment
Agent
Eye mov commands
Finger mov commands
Bounded
vision
Reward
function
Max speed
and
min errors
Finger and
eye positions
Motor
system
Perception
Internal
environment
Eye mov
Finger mov
Generates
moment-by-
moment traces,
from which
summary
statistics can be
computed
13. Computational Rationality as a Theory of Interaction
Comparison with human data
Jokinen et al. CHI’21
Human data Model predictions
The goal in CR is
to accurately
predict human
behavior
14. Computational Rationality as a Theory of Interaction
Significant progress last 5 years
147 papers found
87 peer-reviewed
20 models of HCI tasks
15 used a rigorous method
to find an optimal policy
Six interactive tasks
• Pointing
• Menu selection
• Driving
• Typing
• Multitasking
• Decision-making
15. Computational Rationality as a Theory of Interaction
Opportunity: Explain behavior
[Kangasrääsiö et al. CHI’17, Cognitive Science 2019]
Inverse modeling with likelihood-free inference Bayesian methods
Examples in the paper
17. Computational Rationality as a Theory of Interaction
AI
HCI’s interest is distinct
HCI
Supporting user-
centered design
and engineering
Cognitive
science
Human
intelligence
General
intelligence
18. Computational Rationality as a Theory of Interaction
Agenda: From micro- to macro-HCI
Motivational dynamics
Human learning
Situations
Social interaction