59,661 research outputs found
Model Cards for Model Reporting
Trained machine learning models are increasingly used to perform high-impact
tasks in areas such as law enforcement, medicine, education, and employment. In
order to clarify the intended use cases of machine learning models and minimize
their usage in contexts for which they are not well suited, we recommend that
released models be accompanied by documentation detailing their performance
characteristics. In this paper, we propose a framework that we call model
cards, to encourage such transparent model reporting. Model cards are short
documents accompanying trained machine learning models that provide benchmarked
evaluation in a variety of conditions, such as across different cultural,
demographic, or phenotypic groups (e.g., race, geographic location, sex,
Fitzpatrick skin type) and intersectional groups (e.g., age and race, or sex
and Fitzpatrick skin type) that are relevant to the intended application
domains. Model cards also disclose the context in which models are intended to
be used, details of the performance evaluation procedures, and other relevant
information. While we focus primarily on human-centered machine learning models
in the application fields of computer vision and natural language processing,
this framework can be used to document any trained machine learning model. To
solidify the concept, we provide cards for two supervised models: One trained
to detect smiling faces in images, and one trained to detect toxic comments in
text. We propose model cards as a step towards the responsible democratization
of machine learning and related AI technology, increasing transparency into how
well AI technology works. We hope this work encourages those releasing trained
machine learning models to accompany model releases with similar detailed
evaluation numbers and other relevant documentation
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Factors in human recognition of timbre lexicons generated by data clustering
Since the development of sound recording technologies, the palette of sound timbres available for music creation was extended way beyond traditional musical instruments. The organization and categorization of timbre has been a common endeavor. The availability of large databases of sound clips provides an opportunity for obtaining datadriven timbre categorizations via content-based clustering. In this article we describe an experiment aimed at understanding what factors influence the process of learning a given clustering of sound samples. We clustered a large database of short sound clips, and analyzed the success of participants in assigning sounds to the “correct” clusters after listening to a few examples of each. The results of the experiment suggest a number of relevant factors related both to the strategies followed by users and to the quality measures of the clustering solution, which can guide the design of creative applications based on audio clip clustering
Big data for monitoring educational systems
This report considers “how advances in big data are likely to transform the context and methodology of monitoring educational systems within a long-term perspective (10-30 years) and impact the evidence based policy development in the sector”, big data are “large amounts of different types of data produced with high velocity from a high number of various types of sources.” Five independent experts were commissioned by Ecorys, responding to themes of: students' privacy, educational equity and efficiency, student tracking, assessment and skills. The experts were asked to consider the “macro perspective on governance on educational systems at all levels from primary, secondary education and tertiary – the latter covering all aspects of tertiary from further, to higher, and to VET”, prioritising primary and secondary levels of education
Can Computers Create Art?
This essay discusses whether computers, using Artificial Intelligence (AI),
could create art. First, the history of technologies that automated aspects of
art is surveyed, including photography and animation. In each case, there were
initial fears and denial of the technology, followed by a blossoming of new
creative and professional opportunities for artists. The current hype and
reality of Artificial Intelligence (AI) tools for art making is then discussed,
together with predictions about how AI tools will be used. It is then
speculated about whether it could ever happen that AI systems could be credited
with authorship of artwork. It is theorized that art is something created by
social agents, and so computers cannot be credited with authorship of art in
our current understanding. A few ways that this could change are also
hypothesized.Comment: to appear in Arts, special issue on Machine as Artist (21st Century
The Rhetoric of Video Games
Part of the Volume on the Ecology of Games: Connecting Youth, Games, and Learning Bogost's chapter offers an introduction to rhetoric in games. First he looks at the way games and their rules embody cultural values, following the work of Brian Sutton-Smith and looking in particular at a few examples from international sports. Then he discusses the relationship between games and ideology, showing how game play can unpack and expose deeply engrained social, cultural, and political assumptions. Finally he discusses the ways videogames make arguments. Drawing on the history of rhetoric, Bogost introduces a notion he calls "procedural rhetoric," the art of persuasion through rule-based representations and interactions
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