Enterprise AI Analysis
Artificial creativity: can there be creativity without cognition?
This paper addresses the question of whether artificial systems can be considered creative in the absence of cognition. It advances a novel perspective by grounding the analysis on a foundational premise: generative AI systems, such as LLMs and GMIs, are non-cognitive. This distinction is established through the application of the Minimal Cognitive Grid (MCG), offering a more precise entry point into the creativity debate. Despite their non-cognitive nature, these systems produce outputs that meet standard criteria for creativity—novelty and usefulness—and reproduce, in functional terms, the stages of human creative processes. A comparative analysis with the Wallas-Jaoui model supports this claim. However, the absence of intentionality and authenticity limits any attribution of genuine creativity. So, how can we define Artificial Creativity? To resolve this, the paper introduces a minimal definition of artificial creativity as a non-cognitive, non-intentional, and non-authentic generative mechanism. This is the first attempt to define the concept directly, rather than by exclusion. The definition clarifies the theoretical boundaries between natural and artificial creativity, avoids anthropocentric bias, and establishes a foundation for future research in computational creativity and philosophy of AI.
Author: Matteo Da Pelo | Journal: AI & SOCIETY | Publication Date: October 16, 2025
Strategic Implications for Enterprise AI Adoption
Understanding the non-cognitive nature and distinct creative mechanisms of Generative AI is crucial for strategic enterprise adoption. This analysis provides a foundation for evaluating AI's true capabilities and limitations in creative domains, guiding responsible implementation and fostering innovation.
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Deep Analysis & Enterprise Applications
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Cognition & AI
Creativity Mechanisms
Defining Artificial Creativity
This section delves into the foundational argument that LLMs and GMIs are non-cognitive, applying the Minimal Cognitive Grid (MCG) to assess their limitations compared to human cognition.
Non-Cognitive
AI systems are definitively classified as non-cognitive by the MCG.
The Minimal Cognitive Grid (MCG)
The MCG provides a structured framework to evaluate the cognitive plausibility of AI systems. It assesses balance between functionalism and structuralism, generality, and performance match. Applied to GPT-4, it reveals a purely functional system lacking natural cognitive architecture, thus ruling out cognitive status.
| Aspect | Human Cognition | Generative AI (LLMs/GMIs) |
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| Intentionality |
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| Learning |
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| Creativity Origin |
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This section examines how AI systems replicate or functionally align with stages of human creative processes, using models like Wallas-Jaoui as a neutral benchmark.
AI's Creative Mechanism Stages
Impregnation (Training Data)
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Incubation (Model Learning)
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Illumination (Prompt Activation)
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Verification (Discriminator Feedback / Human Eval)
Functional Equivalence, Not Cognitive Identity
AI systems, despite their non-cognitive nature, functionally reproduce stages of creativity. For instance, 'impregnation' aligns with training on datasets, and 'illumination' with prompt activation. This demonstrates a mechanistic, non-cognitive replication of creative processes.
Pseudomnesia: The Electrician
Boris Eldagsen's AI-generated image, 'Pseudomnesia: The Electrician,' won a major photography award before he revealed its AI origin. This case highlights how AI-generated art can achieve a level of aesthetic and conceptual sophistication indistinguishable from human-created art, challenging assumptions about human intentionality as a prerequisite for artistic value. It demonstrates the product's creative impact, independent of the creator's cognition.
This section introduces the novel definition of artificial creativity as a non-cognitive, non-intentional, and non-authentic generative mechanism, outlining its implications for future research.
Non-Intentional
AI creativity is fundamentally devoid of human-like intentions.
A New Definition for a New Era
Artificial creativity is defined as a non-cognitive, non-intentional, and non-authentic generative mechanism. This direct definition, rather than by exclusion, provides a clear theoretical boundary between natural and artificial creativity, fostering anthropocentric bias.
| Feature | Natural Creativity | Artificial Creativity |
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| Authenticity |
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Your AI Integration Roadmap
Our structured approach ensures a seamless and effective integration of AI into your enterprise, maximizing creative potential while addressing ethical and operational considerations.
Phase 1: Discovery & Strategy
Assess current creative workflows, identify AI opportunities, and define strategic objectives. This includes a comprehensive audit of existing systems and data infrastructure.
Phase 2: Pilot & Proof-of-Concept
Develop and deploy a small-scale AI pilot in a selected creative domain. Evaluate performance against defined metrics and gather user feedback for refinement.
Phase 3: Scaled Deployment & Integration
Integrate AI solutions across relevant enterprise creative and operational units. Establish robust monitoring, maintenance, and continuous improvement protocols.
Phase 4: Training & Adoption
Provide comprehensive training to teams on new AI tools and workflows. Foster a culture of AI-assisted creativity and iterative innovation.
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