Artificial intelligence and the internal processes of creativity

• URL: http://arxiv.org/abs/2412.04366v2
• Date: Fri, 06 Dec 2024 17:31:22 GMT
• Title: Artificial intelligence and the internal processes of creativity
• Authors: Jaan Aru,
• Abstract summary: This paper explores the neurobiological machinery that underlies the internal processes of creativity.<n>It is concluded that although the products of artificial and human creativity can be similar, the internal processes are different.<n>The paper also discusses how AI may negatively affect the internal processes of human creativity, such as the development of skills, the integration of knowledge, and the diversity of ideas.
• Score: 1.3597551064547502
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Artificial intelligence (AI) systems capable of generating creative outputs are reshaping our understanding of creativity. This shift presents an opportunity for creativity researchers to reevaluate the key components of the creative process. In particular, the advanced capabilities of AI underscore the importance of studying the internal processes of creativity. This paper explores the neurobiological machinery that underlies these internal processes and describes the experiential component of creativity. It is concluded that although the products of artificial and human creativity can be similar, the internal processes are different. The paper also discusses how AI may negatively affect the internal processes of human creativity, such as the development of skills, the integration of knowledge, and the diversity of ideas.

Related papers

• Cooking Up Creativity: A Cognitively-Inspired Approach for Enhancing LLM Creativity through Structured Representations [53.950760059792614]
  Large Language Models (LLMs) excel at countless tasks, yet struggle with creativity. We introduce a novel approach that couples LLMs with structured representations and cognitively inspired manipulations to generate more creative and diverse ideas. We demonstrate our approach in the culinary domain with DishCOVER, a model that generates creative recipes.
  arXiv  Detail & Related papers  (2025-04-29T11:13:06Z)
• Probing and Inducing Combinational Creativity in Vision-Language Models [52.76981145923602]
  Recent advances in Vision-Language Models (VLMs) have sparked debate about whether their outputs reflect combinational creativity. We propose the Identification-Explanation-Implication (IEI) framework, which decomposes creative processes into three levels. To validate this framework, we curate CreativeMashup, a high-quality dataset of 666 artist-generated visual mashups annotated according to the IEI framework.
  arXiv  Detail & Related papers  (2025-04-17T17:38:18Z)
• Initial Development and Evaluation of the Creative Artificial Intelligence through Recurring Developments and Determinations (CAIRDD) System [0.0]
  Large language models (LLMs) provide a facsimile of creativity and the appearance of sentience, while not actually being either creative or sentient. This paper proposes a technique for enhancing LLM output creativity via an iterative process of concept injection and refinement.
  arXiv  Detail & Related papers  (2024-09-03T21:04:07Z)
• On the stochastics of human and artificial creativity [0.0]
  We argue that achieving human-level intelligence in computers requires also human-level creativity. We develop a statistical representation of human creativity, incorporating prior insights from theory, psychology, philosophy, neuroscience, and chaos theory. Our analysis includes modern AI algorithms such as reinforcement learning, diffusion models, and large language models, addressing to what extent they measure up to human level creativity.
  arXiv  Detail & Related papers  (2024-03-03T10:38:57Z)
• Can AI Be as Creative as Humans? [84.43873277557852]
  We prove in theory that AI can be as creative as humans under the condition that it can properly fit the data generated by human creators. The debate on AI's creativity is reduced into the question of its ability to fit a sufficient amount of data.
  arXiv  Detail & Related papers  (2024-01-03T08:49:12Z)
• Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment [65.268245109828]
  We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology. We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors. The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
  arXiv  Detail & Related papers  (2023-12-01T05:20:57Z)
• A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization [55.11642177631929]
  Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images. We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
  arXiv  Detail & Related papers  (2023-10-14T23:28:48Z)
• AI and the creative realm: A short review of current and future applications [2.1320960069210484]
  This study explores the concept of creativity and artificial intelligence (AI) The development of more sophisticated AI models and the proliferation of human-computer interaction tools have opened up new possibilities for AI in artistic creation.
  arXiv  Detail & Related papers  (2023-06-01T12:28:08Z)
• Designing Participatory AI: Creative Professionals' Worries and Expectations about Generative AI [8.379286663107845]
  Generative AI, i.e., the group of technologies that automatically generate visual or written content based on text prompts, has undergone a leap in complexity and become widely available within just a few years. This paper presents the results of a qualitative survey investigating how creative professionals think about generative AI.
  arXiv  Detail & Related papers  (2023-03-15T20:57:03Z)
• From Psychological Curiosity to Artificial Curiosity: Curiosity-Driven Learning in Artificial Intelligence Tasks [56.20123080771364]
  Psychological curiosity plays a significant role in human intelligence to enhance learning through exploration and information acquisition. In the Artificial Intelligence (AI) community, artificial curiosity provides a natural intrinsic motivation for efficient learning. CDL has become increasingly popular, where agents are self-motivated to learn novel knowledge.
  arXiv  Detail & Related papers  (2022-01-20T17:07:03Z)

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.