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Dagstuhl Reports, Volume 12, Issue 6
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Dagstuhl Reports, Volume 12
Journal: Dagstuhl Reports (DagRep)
Publication Details
- published at: 2023-01-19
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- DBLP: db/journals/dagstuhl-reports/dagstuhl-reports12
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Complete Issue
Dagstuhl Reports, Volume 12, Issue 6, June 2022, Complete Issue
Abstract
Dagstuhl Reports, Volume 12, Issue 6, June 2019, Complete Issue
Cite as
Dagstuhl Reports, Volume 12, Issue 6, pp. 1-119, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
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@Article{DagRep.12.6,
title = {{Dagstuhl Reports, Volume 12, Issue 6, June 2022, Complete Issue}},
pages = {1--119},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2023},
volume = {12},
number = {6},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.12.6},
URN = {urn:nbn:de:0030-drops-174512},
doi = {10.4230/DagRep.12.6},
annote = {Keywords: Dagstuhl Reports, Volume 12, Issue 6, June 2019, Complete Issue}
}
Document
Front Matter
Dagstuhl Reports, Table of Contents, Volume 12, Issue 6, 2022
Abstract
Dagstuhl Reports, Table of Contents, Volume 12, Issue 6, 2022
Cite as
Dagstuhl Reports, Volume 12, Issue 6, pp. i-ii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
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@Article{DagRep.12.6.i,
title = {{Dagstuhl Reports, Table of Contents, Volume 12, Issue 6, 2022}},
pages = {i--ii},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2023},
volume = {12},
number = {6},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.12.6.i},
URN = {urn:nbn:de:0030-drops-174526},
doi = {10.4230/DagRep.12.6.i},
annote = {Keywords: Table of Contents, Frontmatter}
}
Document
Theories of Programming (Dagstuhl Seminar 22231)
Abstract
Much of computer science research focuses on techniques to make programming easier, better, less error prone, more powerful, and even more just. But rarely do we try to explain any of these challenges. Why is programming hard? Why is it slow? Why is it error prone? Why is it powerful? How does it do harm? These why and how questions are what motivated the Dagstuhl Seminar 22231 on Theories of Programming. This seminar brought together 28 CS researchers from domains most concerned with programming human and social activities: software engineering, programming languages, human-computer interaction, and computing education. Together, we sketched new theories of programming and considered the role of theories more broadly in programming.
Cite as
Thomas D. LaToza, Amy Ko, David C. Shepherd, Dag Sjøberg, and Benjamin Xie. Theories of Programming (Dagstuhl Seminar 22231). In Dagstuhl Reports, Volume 12, Issue 6, pp. 1-13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
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@Article{latoza_et_al:DagRep.12.6.1,
author = {LaToza, Thomas D. and Ko, Amy and Shepherd, David C. and Sj{\o}berg, Dag and Xie, Benjamin},
title = {{Theories of Programming (Dagstuhl Seminar 22231)}},
pages = {1--13},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2023},
volume = {12},
number = {6},
editor = {LaToza, Thomas D. and Ko, Amy and Shepherd, David C. and Sj{\o}berg, Dag and Xie, Benjamin},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.12.6.1},
URN = {urn:nbn:de:0030-drops-174533},
doi = {10.4230/DagRep.12.6.1},
annote = {Keywords: computing education, human-computer interaction, programming languages, software engineering, theories of programming}
}
Document
Efficient and Equitable Natural Language Processing in the Age of Deep Learning (Dagstuhl Seminar 22232)
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 22232 "Efficient and Equitable Natural Language Processing in the Age of Deep Learning". Since 2012, the field of artificial intelligence (AI) has reported remarkable progress on a broad range of capabilities including object recognition, game playing, speech recognition, and machine translation. Much of this progress has been achieved by increasingly large and computationally intensive deep learning models: training costs for state-of-the-art deep learning models have increased 300,000 times between 2012 and 2018 [1]. Perhaps the epitome of this trend is the subfield of natural language processing (NLP) that over the past three years has experienced even sharper growth in model size and corresponding computational requirements in the word embedding approaches (e.g. ELMo, BERT, openGPT-2, Megatron-LM, T5, and GPT-3, one of the largest models ever trained with 175B dense parameters) that are now the basic building blocks of nearly all NLP models. Recent studies indicate that this trend is both environmentally unfriendly and prohibitively expensive, raising barriers to participation in NLP research [2,3]. The goal of this seminar was to mitigate these concerns and promote equity of access in NLP.
References.
[1] D. Amodei and D. Hernandez. 2018. AI and Compute. https://openai.com/blog/ai-and-compute
[2] R. Schwartz, D. Dodge, N. A. Smith, and O. Etzioni. 2020. Green AI. Communications of the ACM (CACM)
[3] E. Strubell, A. Ganesh, and A. McCallum. 2019. Energy and Policy Considerations for Deep Learning in NLP. In Proc. of ACL.
Cite as
Jesse Dodge, Iryna Gurevych, Roy Schwartz, Emma Strubell, and Betty van Aken. Efficient and Equitable Natural Language Processing in the Age of Deep Learning (Dagstuhl Seminar 22232). In Dagstuhl Reports, Volume 12, Issue 6, pp. 14-27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
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@Article{dodge_et_al:DagRep.12.6.14,
author = {Dodge, Jesse and Gurevych, Iryna and Schwartz, Roy and Strubell, Emma and van Aken, Betty},
title = {{Efficient and Equitable Natural Language Processing in the Age of Deep Learning (Dagstuhl Seminar 22232)}},
pages = {14--27},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2023},
volume = {12},
number = {6},
editor = {Dodge, Jesse and Gurevych, Iryna and Schwartz, Roy and Strubell, Emma and van Aken, Betty},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.12.6.14},
URN = {urn:nbn:de:0030-drops-174549},
doi = {10.4230/DagRep.12.6.14},
annote = {Keywords: deep learning, efficiency, equity, natural language processing (nlp)}
}
Document
Human-Game AI Interaction (Dagstuhl Seminar 22251)
Abstract
People interact with semi-intelligent machines during their daily lives. They desire systems to respond intelligently to requests. While improvements to the interaction between humans and AI have been made over the years, these systems are a long way from responding like a human partner. Virtual (game) worlds are an ideal environment in which to experiment with the interaction between humans and AI, due to their similarity with real world environments and the presence of agents that represent "real people" that make decisions and interact among them.
In recent years, the number of ways in which players can interact with games have increased considerably: from the traditional mouse, keyboard, and controller, to responding to natural movements, facial expressions, voice, eye movements and brain signals, among others. This seminar brought together scientists, researchers, and industrial developers who specialize in intelligent interaction between humans and computer agents in virtual (game) environments. This report documents the program and its outcomes.
Cite as
Dan Ashlock, Setareh Maghsudi, Diego Perez Liebana, Pieter Spronck, and Manuel Eberhardinger. Human-Game AI Interaction (Dagstuhl Seminar 22251). In Dagstuhl Reports, Volume 12, Issue 6, pp. 28-82, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
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@Article{ashlock_et_al:DagRep.12.6.28,
author = {Ashlock, Dan and Maghsudi, Setareh and Liebana, Diego Perez and Spronck, Pieter and Eberhardinger, Manuel},
title = {{Human-Game AI Interaction (Dagstuhl Seminar 22251)}},
pages = {28--82},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2023},
volume = {12},
number = {6},
editor = {Ashlock, Dan and Maghsudi, Setareh and Liebana, Diego Perez and Spronck, Pieter and Eberhardinger, Manuel},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.12.6.28},
URN = {urn:nbn:de:0030-drops-174550},
doi = {10.4230/DagRep.12.6.28},
annote = {Keywords: Computational intelligence, artificial intelligence, games, modeling, interaction}
}
Document
Visualization Empowerment: How to Teach and Learn Data Visualization (Dagstuhl Seminar 22261)
Abstract
Data visualization is becoming an important asset for a data-literate, informed, and critical society. Despite the variety of existing resources to teach theories and practical skills in this domain, little is known about 1) how learning processes in the context of visualization unfold and 2) best practices for engaging and teaching data visualization to diverse audiences and in different contexts. This Dagstuhl Seminar invited practitioners, researchers, and teachers from the areas of visualization, design, education and cognitive psychology to explore these questions from multiple perspectives. Through a range of practical activities, talks, and discussions, we have begun characterizing and classifying teaching methodologies. We have redacted a pedagogical manifesto, and started formalizing the concept of improvisation with visualization in the context of teaching and learning. We have also interrogated creativity as an important aspect of visualization teaching and learning and explored links between data physicalization and visualization teaching activities. Across these different themes, we have begun to map out the challenges of visualization teaching and learning and the opportunities for research and practice in this area.
Cite as
Benjamin Bach, Sheelagh Carpendale, Uta Hinrichs, and Samuel Huron. Visualization Empowerment: How to Teach and Learn Data Visualization (Dagstuhl Seminar 22261). In Dagstuhl Reports, Volume 12, Issue 6, pp. 83-111, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
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@Article{bach_et_al:DagRep.12.6.83,
author = {Bach, Benjamin and Carpendale, Sheelagh and Hinrichs, Uta and Huron, Samuel},
title = {{Visualization Empowerment: How to Teach and Learn Data Visualization (Dagstuhl Seminar 22261)}},
pages = {83--111},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2023},
volume = {12},
number = {6},
editor = {Bach, Benjamin and Carpendale, Sheelagh and Hinrichs, Uta and Huron, Samuel},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.12.6.83},
URN = {urn:nbn:de:0030-drops-174568},
doi = {10.4230/DagRep.12.6.83},
annote = {Keywords: Information Visualization, Visualization Literacy, Data Literacy, Education}
}
Document
Human-Centered Artificial Intelligence (Dagstuhl Seminar 22262)
Abstract
This report documents the program and the outcomes of Dagstuhl Perspectives Workshop 22262 "Human-Centered Artificial Intelligence".
The goal of this Dagstuhl Perspectives Workshops is to provide the scientific and technological foundations for designing and deploying hybrid human-centered AI systems that work in partnership with human beings and that enhance human capabilities rather than replace human intelligence. Fundamentally new solutions are needed for core research problems in AI and human-computer interaction (HCI), especially to help people understand actions recommended or performed by AI systems and to facilitate meaningful interaction between humans and AI systems. Specific challenges include: learning complex world models; building effective and explainable machine learning systems; developing human-controllable intelligent systems; adapting AI systems to dynamic, open-ended real-world environments (in particular robots and autonomous systems); achieving in-depth understanding of humans and complex social contexts; and enabling self-reflection within AI systems.
Cite as
Wendy E. Mackay, John Shawe-Taylor, and Frank van Harmelen. Human-Centered Artificial Intelligence (Dagstuhl Seminar 22262). In Dagstuhl Reports, Volume 12, Issue 6, pp. 112-117, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
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@Article{mackay_et_al:DagRep.12.6.112,
author = {Mackay, Wendy E. and Shawe-Taylor, John and van Harmelen, Frank},
title = {{Human-Centered Artificial Intelligence (Dagstuhl Seminar 22262)}},
pages = {112--117},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2023},
volume = {12},
number = {6},
editor = {Mackay, Wendy E. and Shawe-Taylor, John and van Harmelen, Frank},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.12.6.112},
URN = {urn:nbn:de:0030-drops-174579},
doi = {10.4230/DagRep.12.6.112},
annote = {Keywords: Human-centered Artificial Intelligence, Human-Computer Interaction, Hybrid Intelligence}
}