On the stochastics of human and artificial creativity
- URL: http://arxiv.org/abs/2403.06996v1
- Date: Sun, 3 Mar 2024 10:38:57 GMT
- Title: On the stochastics of human and artificial creativity
- Authors: Solve Sæbø, Helge Brovold,
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: What constitutes human creativity, and is it possible for computers to exhibit genuine creativity? We argue that achieving human-level intelligence in computers, or so-called Artificial General Intelligence, necessitates attaining also human-level creativity. We contribute to this discussion by developing a statistical representation of human creativity, incorporating prior insights from stochastic theory, psychology, philosophy, neuroscience, and chaos theory. This highlights the stochastic nature of the human creative process, which includes both a bias guided, random proposal step, and an evaluation step depending on a flexible or transformable bias structure. The acquired representation of human creativity is subsequently used to assess the creativity levels of various contemporary AI systems. 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. We conclude that these technologies currently lack the capability for autonomous creative action at a human level.
Related papers
- Creativity in AI: Progresses and Challenges [17.03526787878041]
We study the creative capabilities of AI systems, focusing on creative problem-solving, linguistic, artistic, and scientific creativity.
Our review suggests that while the latest AI models are largely capable of producing linguistically and artistically creative outputs, they struggle with tasks that require creative problem-solving.
We highlight the need for a comprehensive evaluation of creativity that is process-driven and considers several dimensions of creativity.
arXiv Detail & Related papers (2024-10-22T17:43:39Z) - Can AI Enhance its Creativity to Beat Humans ? [0.0]
This study investigates the creative performance of artificial intelligence (AI) compared to humans.
Human external evaluators have scored creative outputs generated by humans and AI.
Results suggest that integrating human feedback is crucial for maximizing AI's creative potential.
arXiv Detail & Related papers (2024-09-27T14:19:07Z) - 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) - AI for Mathematics: A Cognitive Science Perspective [86.02346372284292]
Mathematics is one of the most powerful conceptual systems developed and used by the human species.
Rapid progress in AI, particularly propelled by advances in large language models (LLMs), has sparked renewed, widespread interest in building such systems.
arXiv Detail & Related papers (2023-10-19T02:00:31Z) - 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) - Machine Psychology [54.287802134327485]
We argue that a fruitful direction for research is engaging large language models in behavioral experiments inspired by psychology.
We highlight theoretical perspectives, experimental paradigms, and computational analysis techniques that this approach brings to the table.
It paves the way for a "machine psychology" for generative artificial intelligence (AI) that goes beyond performance benchmarks.
arXiv Detail & Related papers (2023-03-24T13:24:41Z) - World Models and Predictive Coding for Cognitive and Developmental
Robotics: Frontiers and Challenges [51.92834011423463]
We focus on the two concepts of world models and predictive coding.
In neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment.
arXiv Detail & Related papers (2023-01-14T06:38:14Z) - Pathway to Future Symbiotic Creativity [76.20798455931603]
We propose a classification of the creative system with a hierarchy of 5 classes, showing the pathway of creativity evolving from a mimic-human artist to a Machine artist in its own right.
In art creation, it is necessary for machines to understand humans' mental states, including desires, appreciation, and emotions, humans also need to understand machines' creative capabilities and limitations.
We propose a novel framework for building future Machine artists, which comes with the philosophy that a human-compatible AI system should be based on the "human-in-the-loop" principle.
arXiv Detail & Related papers (2022-08-18T15:12:02Z) - Creativity in the era of artificial intelligence [1.8275108630751844]
We aim to provide a new perspective on the question of creativity at the era of AI, by blurring the frontier between social and computational sciences.
We argue that the objective of trying to purely mimic human creative traits towards a self-contained ex-nihilo generative machine would be highly counterproductive.
arXiv Detail & Related papers (2020-08-13T15:07:34Z) - The societal and ethical relevance of computational creativity [0.4297070083645048]
We characterize creativity in very broad philosophical terms, encompassing natural, existential, and social creative processes.
We explain why creativity is instrumental for advancing human well-being in the long term.
There is an argument for ethics to be more hospitable to creativity-enabling AI.
arXiv Detail & Related papers (2020-07-23T12:39:10Z)
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.