Performing Creativity With Computational Tools
- URL: http://arxiv.org/abs/2103.05533v2
- Date: Wed, 10 Mar 2021 10:38:26 GMT
- Title: Performing Creativity With Computational Tools
- Authors: Daniel Lopes, J\'essica Parente, Pedro Silva, Lic\'inio Roque,
Penousal machado
- Abstract summary: The study was driven by a grounded theory methodology, applied to a set of semi-structured interviews.
The results suggest some scenarios in which it is or it is not worth investing in the development of new intelligent creativity-aiding tools.
- Score: 2.2856845877179492
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The introduction of new tools in people's workflow has always been promotive
of new creative paths. This paper discusses the impact of using computational
tools in the performance of creative tasks, especially focusing on graphic
design. The study was driven by a grounded theory methodology, applied to a set
of semi-structured interviews, made to twelve people working in the areas of
graphic design, data science, computer art, music and data visualisation. Among
other questions, the results suggest some scenarios in which it is or it is not
worth investing in the development of new intelligent creativity-aiding tools.
Related papers
- How Do Hackathons Foster Creativity? Towards AI Collaborative Evaluation of Creativity at Scale [47.73894679677285]
We conduct a computational analysis of 193,353 hackathon projects.
We identify means for organizers to foster creativity in hackathons.
We explore the use of large language models to augment the evaluation of creative outcomes.
arXiv Detail & Related papers (2025-03-06T10:17:52Z) - A Framework for Collaborating a Large Language Model Tool in Brainstorming for Triggering Creative Thoughts [2.709166684084394]
This study proposes a framework called GPS, which employs goals, prompts, and strategies to guide designers to systematically work with an LLM tool for improving the creativity of ideas generated during brainstorming.
Our framework, tested through a design example and a case study, demonstrates its effectiveness in stimulating creativity and its seamless LLM tool integration into design practices.
arXiv Detail & Related papers (2024-10-10T13:39:27Z) - What's Next? Exploring Utilization, Challenges, and Future Directions of AI-Generated Image Tools in Graphic Design [2.0616038498705858]
This study conducted semi-structured interviews with seven designers of varying experience levels to understand their current usage, challenges, and future needs for AI-generated image tools in graphic design.
As our findings suggest, AI tools serve as creative partners in design, enhancing human creativity, offering strategic insights, and fostering team collaboration and communication.
The findings provide guiding recommendations for the future development of AI-generated image tools, aimed at helping engineers optimize these tools to better meet the needs of graphic designers.
arXiv Detail & Related papers (2024-06-19T10:51:56Z) - From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models [98.41645229835493]
Data visualization in the form of charts plays a pivotal role in data analysis, offering critical insights and aiding in informed decision-making.
Large foundation models, such as large language models, have revolutionized various natural language processing tasks.
This survey paper serves as a comprehensive resource for researchers and practitioners in the fields of natural language processing, computer vision, and data analysis.
arXiv Detail & Related papers (2024-03-18T17:57:09Z) - CLOVA: A Closed-Loop Visual Assistant with Tool Usage and Update [69.59482029810198]
CLOVA is a Closed-Loop Visual Assistant that operates within a framework encompassing inference, reflection, and learning phases.
Results demonstrate that CLOVA surpasses existing tool-usage methods by 5% in visual question answering and multiple-image reasoning, by 10% in knowledge tagging, and by 20% in image editing.
arXiv Detail & Related papers (2023-12-18T03:34:07Z) - Human Machine Co-Creation. A Complementary Cognitive Approach to
Creative Character Design Process Using GANs [0.0]
Two neural networks compete to generate new visual contents indistinguishable from the original dataset.
The proposed approach aims to inform the process of perceiving, knowing, and making.
The machine generated concepts are used as a launching platform for character designers to conceptualize new characters.
arXiv Detail & Related papers (2023-11-23T12:18:39Z) - Augmenting Character Designers Creativity Using Generative Adversarial
Networks [0.0]
Generative Adversarial Networks (GANs) continue to attract the attention of researchers in different fields.
Most recent GANs are focused on realism, however, generating hyper-realistic output is not a priority for some domains.
We present a comparison between different GAN architectures and their performance when trained from scratch on a new visual characters dataset.
We also explore alternative techniques, such as transfer learning and data augmentation, to overcome computational resource limitations.
arXiv Detail & Related papers (2023-05-28T10:52:03Z) - LLM-based Interaction for Content Generation: A Case Study on the
Perception of Employees in an IT department [85.1523466539595]
This paper presents a questionnaire survey to identify the intention to use generative tools by employees of an IT company.
Our results indicate a rather average acceptability of generative tools, although the more useful the tool is perceived to be, the higher the intention seems to be.
Our analyses suggest that the frequency of use of generative tools is likely to be a key factor in understanding how employees perceive these tools in the context of their work.
arXiv Detail & Related papers (2023-04-18T15:35:43Z) - Tool Learning with Foundation Models [158.8640687353623]
With the advent of foundation models, AI systems have the potential to be equally adept in tool use as humans.
Despite its immense potential, there is still a lack of a comprehensive understanding of key challenges, opportunities, and future endeavors in this field.
arXiv Detail & Related papers (2023-04-17T15:16:10Z) - Sketch-based Creativity Support Tools using Deep Learning [23.366634691081593]
Recent developments in deep-learning models drastically improved machines' ability in understanding and generating visual content.
An exciting area of development explores deep-learning approaches used to model human sketches, opening opportunities for creative applications.
This chapter describes three fundamental steps in developing deep-learning-driven creativity support tools that consumes and generates sketches.
arXiv Detail & Related papers (2021-11-19T00:57:43Z) - Explaining Creative Artifacts [69.86890599471202]
We develop an inverse problem formulation to deconstruct the products of and compositional creativity into associative chains.
In particular, our formulation is structured as solving a traveling salesman problem through a knowledge graph of associative elements.
arXiv Detail & Related papers (2020-10-14T14:32:38Z) - A Review on Intelligent Object Perception Methods Combining
Knowledge-based Reasoning and Machine Learning [60.335974351919816]
Object perception is a fundamental sub-field of Computer Vision.
Recent works seek ways to integrate knowledge engineering in order to expand the level of intelligence of the visual interpretation of objects.
arXiv Detail & Related papers (2019-12-26T13:26:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.