Kyungja, by Eunsu Kang

Libratus, a Poker playing Neural Network developed by Carnegie Mellon University, applies Reinforcement Learning techniques along with standard backpropagation and temporal delay techniques in order to win against Poker players across the world, including the winners of past Poker Grand Tournaments. Some time ago I came across Libratus, a bot made by Carnegie Mellon for playing heads up (1 vs 1) no limit hold’em. Libratus proved its worth by beating some of the world’s best poker players. Queen mary cruise ship casino.

Sunday, October 18

15:00pm - 19:00pm (Montreal Time, GMT-4, EST)

ISEA2020 - Why Sentience

Speakers

Schedule

Questions

Participants

Call for Participation

Metrics

Organizers

How do we make a creative machine? This workshop follows a series of studies conducted in the classroom setting at CMU and UC San Diego. For ISEA, participants will collectively find a definition of creativity specific to machines, addressing follow up questions: Can we computationally model ambiguity? Would a novelty search result in valuable discoveries? Where is the threshold between randomness and creativity? How do we evaluate the creativity of an algorithm? To answer these questions, participants develop sets of criteria to assess their own and peer groups’ creative AI and ML projects. Although such a human-centered method is subjective, we anticipate discovering ways to describe and interpret dimensions of algorithmic creativity. This half-day workshop engages a broader arts and machine learning community to collaboratively define these quantitative criteria, in a first attempt to collectively establish evaluation metrics for the area of creative AI.

ISEA 2020 is a virtual event and this workshop will be conducted online. Our invited speakers will provide prerecorded video talks and participate in live panel discussions.

NOTE: This page will be updated frequently as we confirm workshop participants.

Introductory slides from Eunsu Kang: Measuring Computational Creativity (pdf)

Allison Parrish (New York University, decontextualize.com)

Devi Parikh (Georgia Tech/Facebook AI Research)

Aaron Hertzmann (Adobe Research)

Roger Dannenberg (Carnegie Mellon University)

Fabrizio Poltronieri (Institute of Creative Technologies at De Montfort University, fabriziopoltronieri.com)

Haru Ji (OCAD University, artificialnature.net/) and Graham Wakefield (York University, artificialnature.net)

Jun-Yan Zhu (Carnegie Mellon University)

Ahmed Elgammal (Rutgers University)

tentative schedule (online videos: invited speaker talks, participants will watch it beforehand)

Time	Activity	Location
3:00 - 3:20	Introduction (20 min)	main room
3:20 - 3:50	Discussion 1 - Elements of Creative AI (30 min)	breakout rooms
3:50 - 4:05	Q & A (15 min)	main room
4:05 - 4:40	Guest Speaker Panel 1 (35min)	main room
4:40 - 5:10	Discussion 2 - Evaluating ML/Art Projects (30 min)	breakout rooms
5:10 - 5:25	Q & A (15 min)	main room
5:25 - 6:10	Guest Speaker Panel 2 (35 min)	main room
6:10 - 6:30	Discussion 3 - Revising Metrics, Evaluation 2 (20 min)	breakout rooms
6:30 - 7:00	Presentation of Results and Q&A	main room

Panel 1: Graham Wakefield, Haru Ji, Fabrizio Poltronieri, Allison Parrish, Aaron Hertzmann

What are your methods and metrics for evaluating the creative AI system that you and your colleagues have developed?
How does the study of creative AI systems inform human creative practice?
How do we attribute (who is responsible for) the creativity in collaborative creative systems?
Do you think there is a difference between creative AI and human creativity? What would be the difference? If not, why not.

Panel 2: Ahmed Elgammal, Devi Parikh, Jun-Yan Zhu, Roger Dannenberg

What are your methods and metrics for evaluating the creative AI system that you and your colleagues have developed?
Do human creative methods provide valuable models for computational creativity? How does human creativity influence computational creativity?
How do we attribute (who is responsible for) the creativity in collaborative creative systems?
How would you define creativity in generative models? If these models are specifically trained to generate new content, does this mean that they are necessarily creative?

Additional Questions

Are all generative systems “creative”? or… Is there a meaningful distinction between generativity and creativity?
How can we contribute to a better definition of “creativity” (which is still fuzzy) using measures that capture computational creativity?
Evaluating a creative work can be considered subjective. Can we (and should we) account for this subjectivity in our artificially intelligent evaluation methods, or should we strive to make objective measures?

This session is an actual working session: together we will collaboratively define metrics for Creative AI.

Tuomas SandholmSuperhuman AI for heads-up no-limit poker: Libratus beats top professionals

Carlos CastellanosPlanetConnect

Austin Leemongyudowondo.com/

Nick Fox-Gieghttps://www.instagram.com/p/BxffQ0RADZ0/

Koosiljahttps://koosil-jadance.com/

Peter Schaldenbrandhttps://pschaldenbrand.github.io/

Kat MustateaWhat is the Value of Art in the Age of Intelligent Machines?

Dana Sperryhttps://vimeo.com/460211868

Kim Barakahttps://www.youtube.com/watch?v=PNzeT8ZsyfM

Rodrigo F. Cádizhttps://creativai-uc.github.io/

Jie-Eun Hwanghttps://openarchive.uosarch.ac.kr/

Libratus Poker Github Game

Joel OngTerra et Venti

Alex MacLeanSwitch Jockey

Claire Jerverthttps://m.youtube.com/watch?v=UKHgOupljmA

Aaron OldenburgDesert Mothers

Amalia FokaThe Invisible Structures of the Artworld

Weihua Zhao404 Not Found

Vít RůžičkaGAN Explorer

Patricia Alves-Oliveirahttps://www.youtube.com/watch?v=e-K3J5UZ9M4&feature=emb_logo

Violaine LafortuneOrganisme No. 17

Donald Craighttps://www.youtube.com/watch?v=hWTwmDpVtdk

Andrew Brownhttps://vimeo.com/142107969

Johnny Diblasihttps://www.design.iastate.edu/news/2020/04/johnny-diblasi-fulbright/

Kiel HoweBaby’s First Image Classification Training Data Set - An Opensource Children’s Book

Jon PadenDadum

Pablo SotresUruguay25

Participants will be updated as they are confirmed.

Participants in this workshop, as a group, will examine a number of Artificial Intelligence (AI) Art projects, and articulate metrics to evaluate dimensions of creativity in those works. Workshop participants are key contributors to this research in Measuring Creative AI, and we plan for every participant to be named on future mCreativeAI publications (website, papers, etc.) as contributors. This is a novel research project with no prior examples as far as we know, and this workshop will be the inaugural event for this effort as this exercise has only been conducted with students previously.

How to participate: If you are interested to participate, please fill out the following google form by Friday, September 25th, midnight Eastern.

Form: https

2021.05