Using Watson for Enhancing Human-Computer Co-Creativity

Design & Intelligence Lab - Georgia Institute of Technology
Using Watson to Enhance
Human-Computer Co-Creativity
Presented by: Bryan Wiltgen
Based on work by: Ashok Goel, Brian Creeden, Mithun Kumble,
Shanu Salunke, Abhinaya Shetty, & Bryan Wiltgen

Acknowledgments
● IBM
○ Watson Engagement Advisor
○ Two IBM Faculty Awards in Cognitive Systems.
● Students in the Georgia Tech’s Spring 2015
CS 4803/8803: Computational Creativity class
● Dianne Fodell for inviting me to speak!

IBM’s Watson
Engagement Advisor
CS 4803/8803:
Computational Creativity
Watson Best Practices
Biologically Inspired Design Apps
Watson in Education

Outline
1. Background
2. Class Projects
3. Conclusions

Background

The Design & Intelligence Lab (http://dilab.gatech.edu)
Faculty
Goel Joyner McGreggor Rugaber
Ph.D. Students
Banerjee Delgado Ehsan Fitzgerald Wiltgen
Awasthy
Azad
Frazer
Kulkarni
Koushik
Sarathy
Shetty
Hartman Tuchez
Spiliopoulou
M.S. Students UG Students

Computational
Creativity
Design
Thinking
Systems
Thinking
Analogical
Thinking
Visual
Thinking
Meta
Thinking
Abductive
Thinking
The Design & Intelligence Lab (http://dilab.gatech.edu)

CS 4803/8803: Computational Creativity Class
Spring 2015
24 Students - 21 graduate, 3 undergraduate
Learning Goals:
1. To become familiar with the literature on computational creativity (concepts,
methods, tasks)
2. To become familiar with the state of art in computational creativity (systems,
techniques, tools)
3. To learn about the processes of designing, developing and deploying
interactive/autonomous creative systems from ideation to realization
4. To acquire experience in designing an interactive creative tools
5. To become an independent thinker in computational creativity.

Class Projects in Computational Creativity Class
Six, self-organized teams of four students each
Goal:
Deeply address a problem in computational creativity,
specifically in the domain of biologically inspired design.

Class Projects in Computational Creativity Class
Phase 1: Initial Learning Phase
1. Select case-study of Biologically Inspired Design
2. Seed Watson with articles
3. Generate and answer questions relative to selected case
4. Train Watson on Q&A pairs
5. Evaluate Watson for answering design questions relevant to selected case
Phase 2: Open-ended Research Phase
● Teams grew the number of documents in our Watson KB
● Teams developed custom-made software
● Teams evaluated aspects of their projects
● Teams wrote reflective design reports and made short videos

Biologically Inspired Design
Core Idea:
Take inspiration from nature to develop creative
solutions to design challenges.

Example of Biologically Inspired Design
Shinkansen 500: Japanese bullet train

IBM’s Watson
(Specifically: Watson Engagement Advisor)
Automated question-answering.
Q&A
Training
Styling
Our Students Inputted the Following:
● About 500 Documents
● About 1200 Questions

Class Projects

Ask Jill
Search for and manage relevant papers
in a literature survey writing context.
The Class Projects
SustArch
Search tool and marketplace for
sustainable architecture.

The Class Projects
Watson BioMaterial
Search for relevant materials.
Watsabi
Automated and community-driven
Q&A for agriculture.

The Class Projects
Twenty Questions
Find relevant document through an iterative, game-inspired process.

The Class Projects
Erasmus
Explore a concept-space.

Conclusions

Watson Best Practices
● Well structured/annotated data is needed and is time-
consuming to produce.
○ Automation
■ Including automated chunking of documents
○ Crowd-Sourcing
○ Wikipedia is a good source.
● Training Watson
○ Bridge questions to link questions together.
○ Crowd-Sourcing
○ Developer-Responsible Feedback Loop
○ 3-Step Approach to Training
■ Train on all possible questions that the corpus papers can answer
■ Train Watson on alternative versions of those questions
■ If first two steps prove insufficient, add additional papers and train on
those.

When creativity emerges from interactions
between humans and computers.
Hypothesis: Human-Watson interaction, when mediated through
technologies like those produced by the students, results in
human-computer co-creativity.
Why?
● Users typically will engage in longer-term interactions.
● Techs added semantics and context to Watson interactions.

Watson in Education
Variety in the Projects
● Topics
○ 5 Bio-inspired Design
■ 3 Domain Independent
■ 2 Targeted specific domains (Resilient
Materials and Built Architecture)
○ 1 Agriculture
● Interaction
○ All supported human-computer interaction.
○ 2 supported human-human interaction.
● All were Internet-enabled.
● 2 were Android apps.
● 1 inspired by a game.
● 1 integrated another IBM technology
(AlchemyAPI).
Exploring Watson for Online
Classroom FAQ
● Goel teaches the Udacity class CS 7637:
Knowledge-based AI: Cognitive Systems as
part of Georgia Tech’s OMSCS program.
● The class uses Piazza for online discussion.
● In Fall 2014:
○ ~6950 Messages
○ ~170 Students
● In Spring 2015:
○ ~11,000 Messages
○ ~240 Students
● We are developing a Watson-powered
technology to automatically answer FAQ’s in
this future offerings of this class.

Thank You For Listening
Bryan Wiltgen
bryan.wiltgen@gatech.edu
Design & Intelligence Lab
http://dilab.gatech.edu
Class Project Video Playlist on YouTube
https://www.youtube.com/playlist?list=PL44rHkM-p0hu5H7oS3OXYgK9qDkVajyqY
Published Version of This Work
Ashok Goel, Brian Creeden, Mithun Kumble, Shanu Salunke, Abhinaya Shetty, & Bryan Wiltgen (2015). Using
Watson for Enhancing Human-Computer Co-Creativity. To appear in procs. of AAAI 2015 Fall Symposium.

Using Watson for Enhancing Human-Computer Co-Creativity

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