7,197 research outputs found
Paranoid Transformer: Reading Narrative of Madness as Computational Approach to Creativity
This papers revisits the receptive theory in context of computational
creativity. It presents a case study of a Paranoid Transformer - a fully
autonomous text generation engine with raw output that could be read as the
narrative of a mad digital persona without any additional human post-filtering.
We describe technical details of the generative system, provide examples of
output and discuss the impact of receptive theory, chance discovery and
simulation of fringe mental state on the understanding of computational
creativity
Framing tension for game generation
Emotional progression in narratives is carefully structured by
human authors to create unexpected and exciting situations,
often culminating in a climactic moment. This paper explores how an autonomous computational designer can create frames of tension which guide the procedural creation of
levels and their soundscapes in a digital horror game. Using
narrative concepts, the autonomous designer can describe an
intended experience that the automated level generator must
adhere to. The level generator interprets this intent, bound
by the possibilities and constraints of the game. The tension
of the generated level guides the allocation of sounds in the
level, using a crowdsourced model of tension.peer-reviewe
Poetry at the first steps of Artificial Intelligence
This paper is about Artificial Intelligence (AI) attempts at writing poetry, usually referred to with the term “poetry generation”. Poetry generation started out from Digital Humanities, which developed out of humanities computing; nowadays, however, it is part of Computational Creativity, a field that tackles several areas of art and science.
In the paper it is examined, first, why poetry was chosen among other literary genres as a field for experimentation. Mention is made to the characteristics of poetry (namely arbitrariness and absurdity) that make it fertile ground for such endeavors and also to various text- and reader-centered literary approaches that favored experimentation even by human poets.
Then, a rough historic look at poetry generation is attempted, followed by a review of the methods employed, either for fun or as academic projects, along Lamb et al.’s (2017) taxonomy which distinguishes between mere poetry generation and result enhancement. Another taxonomy by Gonçalo Oliveira (2017), dividing between form and content issues in poetry generation, is also briefly presented.
The results of poetry generators are evaluated as generally poor and the reasons for this failure are examined: inability of computers to understand any word as a sign with a signified, lack of general intelligence, process- (rather than output-) driven attempts, etc.
Then, computer-like results from a number of human poetic movements are also presented as a juxtaposition: DADA, stream of consciousness, OuLiPo, LangPo, Flarf, blackout/erasure poetry. The equivalence between (i) human poets that are concerned more with experimentation more than with good results and (ii) computer scientists who are process-driven leads to a discussion of the characteristics of humanness, of the possibility of granting future AI personhood and of the need to see our world in terms of a new, more refined ontology
Sonancia : a multi-faceted generator for horror
Fear and tension are the primary emotions elicited by the genre of horror, a peculiar characteristic for media whose sole purpose is to entertain. The audience is often lead into tense and fearful situations, meticulously crafted by the authors using a narrative progression and a combination of visual and auditory stimuli. This paper presents a playable demonstration of the Sonancia system, a multi-faceted content generator for 3D horror games, with the capability of generating levels and their corresponding soundscapes. Designers can also guide the level generation process, by defining an intended progression of tension, which the level generator and sonification will adhere to.peer-reviewe
Considering Human Aspects on Strategies for Designing and Managing Distributed Human Computation
A human computation system can be viewed as a distributed system in which the
processors are humans, called workers. Such systems harness the cognitive power
of a group of workers connected to the Internet to execute relatively simple
tasks, whose solutions, once grouped, solve a problem that systems equipped
with only machines could not solve satisfactorily. Examples of such systems are
Amazon Mechanical Turk and the Zooniverse platform. A human computation
application comprises a group of tasks, each of them can be performed by one
worker. Tasks might have dependencies among each other. In this study, we
propose a theoretical framework to analyze such type of application from a
distributed systems point of view. Our framework is established on three
dimensions that represent different perspectives in which human computation
applications can be approached: quality-of-service requirements, design and
management strategies, and human aspects. By using this framework, we review
human computation in the perspective of programmers seeking to improve the
design of human computation applications and managers seeking to increase the
effectiveness of human computation infrastructures in running such
applications. In doing so, besides integrating and organizing what has been
done in this direction, we also put into perspective the fact that the human
aspects of the workers in such systems introduce new challenges in terms of,
for example, task assignment, dependency management, and fault prevention and
tolerance. We discuss how they are related to distributed systems and other
areas of knowledge.Comment: 3 figures, 1 tabl
Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities
We present a framework for automating generative deep learning with a
specific focus on artistic applications. The framework provides opportunities
to hand over creative responsibilities to a generative system as targets for
automation. For the definition of targets, we adopt core concepts from
automated machine learning and an analysis of generative deep learning
pipelines, both in standard and artistic settings. To motivate the framework,
we argue that automation aligns well with the goal of increasing the creative
responsibility of a generative system, a central theme in computational
creativity research. We understand automation as the challenge of granting a
generative system more creative autonomy, by framing the interaction between
the user and the system as a co-creative process. The development of the
framework is informed by our analysis of the relationship between automation
and creative autonomy. An illustrative example shows how the framework can give
inspiration and guidance in the process of handing over creative
responsibility
Machine Psychology: Investigating Emergent Capabilities and Behavior in Large Language Models Using Psychological Methods
Large language models (LLMs) are currently at the forefront of intertwining
AI systems with human communication and everyday life. Due to rapid
technological advances and their extreme versatility, LLMs nowadays have
millions of users and are at the cusp of being the main go-to technology for
information retrieval, content generation, problem-solving, etc. Therefore, it
is of great importance to thoroughly assess and scrutinize their capabilities.
Due to increasingly complex and novel behavioral patterns in current LLMs, this
can be done by treating them as participants in psychology experiments that
were originally designed to test humans. For this purpose, the paper introduces
a new field of research called "machine psychology". The paper outlines how
different subfields of psychology can inform behavioral tests for LLMs. It
defines methodological standards for machine psychology research, especially by
focusing on policies for prompt designs. Additionally, it describes how
behavioral patterns discovered in LLMs are to be interpreted. In sum, machine
psychology aims to discover emergent abilities in LLMs that cannot be detected
by most traditional natural language processing benchmarks
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